Professor Saheed Ajayi

Purpose Clients in Nigeria have continuously questioned the quality of services being rendered by architectural firms in building projects. This study aims to investigate the areas of service responsible for determining client satisfaction. Design/methodology/approach The study uses questionnaire for data collection on perceived service quality and indicators of clients’ satisfaction. Findings The results of descriptive statistics suggest that efficient analysis and compliance with client brief along with buildability, flexibility and comprehensiveness of the design are the main technical determinants of clients’ satisfaction. Management measures for engendering clients’ satisfaction include team communication and collaboration as well as regular site visits when required. The study established a significant relationship between the perceived service quality and client satisfaction. At technical level, economical design and compliance with budget, buildability, optimal and error-free design and timely delivery have significant correlation with the perception of service quality, which could engender client satisfaction. At management level, collaboration and coordination, integrity and trust, regular site visits and project management knowledge and skills had significant relationships with perceived service quality. Originality/value To improve overall client satisfaction, architects are expected to focus on these factors in the process of service delivery. Architects’ expertise and skills can be further harnessed through continuous training and understanding of the project environment.

Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM.

© 2016 Elsevier Ltd. All rights reserved.In recent times, construction industry is enduring pressure to take drastic steps to minimise waste. Waste intelligence advocates retrospective measures to manage waste after it is produced. Existing waste intelligence based waste management software are fundamentally limited and cannot facilitate stakeholders in controlling wasteful activities. Paradoxically, despite a great amount of effort, the waste being produced by the construction industry is escalating. This undesirable situation motivates a radical change from waste intelligence to waste analytics (in which waste is propose to be tackle proactively right at design through sophisticated big data technologies). This paper highlight that waste minimisation at design (a.k.a. designing-out waste) is data-driven and computationally intensive challenge. The aim of this paper is to propose a Big Data architecture for construction waste analytics. To this end, existing literature on big data technologies is reviewed to identify the critical components of the proposed Big Data based waste analytics architecture. At the crux, graph-based components are used: in particular, a graph database (Neo4J) is adopted to store highly voluminous and diverse datasets. To complement, Spark, a highly resilient graph processing system, is employed. Provision for extensions through Building Information Modelling (BIM) are also considered for synergy and greater adoption. This symbiotic integration of technologies enables a vibrant environment for design exploration and optimisation to tackle construction waste. The main contribution of this paper is that it presents, to the best of our knowledge, the first Big Data based architecture for construction waste analytics. The architecture is validated for exploratory analytics of 200,000 waste disposal records from 900 completed projects. It is revealed that existing waste management software classify the bulk of construction waste as mixed waste, which exposes poor waste data management. The findings of this paper will be of interest, more generally to researchers, who are seeking to develop big data based simulation tools in similar non-trivial applications.

© 2015 Elsevier Ltd. All rights reserved.Owing to its contribution of largest portion of landfill wastes and consumption of about half of mineral resources excavated from nature, construction industry has been pressed to improve its sustainability. Despite an adoption of several waste management strategies, and introduction of various legislative measures, reducing waste generated by the industry remains challenging. In order to understand cultural factors contributing to waste intensiveness of the industry, as well as those preventing effectiveness of existing waste management strategies, this study examines cultural profile of construction industry. Drawing on four focus group discussions with industry experts, the study employs phenomenological approach to explore waste inducing cultural factors. Combining findings from phenomenological research with extant literatures, the study suggests that in order to reduce waste intensiveness of the construction industry, five waste inducing cultural factors need to be changed. These include (i) "make-do" understanding that usually result in "make-do waste" (ii) non-collaborative culture, which results in reworks and other forms of wasteful activities (iii) blame culture, which encourages shifting of waste preventive responsibilities between designers and contractors, (iv) culture of waste behaviour, which encourages belief in waste inevitability, and (v) conservatism, which hinders diffusion of innovation across the industry. Changing these sets of cultural and behavioural activities is not only important for engendering waste management practices; they are requisite for effectiveness of existing strategies. Improvement in the identified areas is also required for overall improvement and general resource efficiency of the construction industry. Thus, this paper advocates cultural shift as a means of reducing waste landfilled by the construction industry, thereby enhancing sustainability and profitability of the industry.

The overall aim of this study is to develop a Building Information Modelling based Deconstructability Assessment Score (BIM-DAS) for determining the extent to which a building could be deconstructed right from the design stage. To achieve this, a review of extant literature was carried out to identify critical design principles influencing effectual building deconstruction and key features for assessing the performance of Design for Deconstruction (DfD). Thereafter, these key features were used to develop BIM-DAS using mathematical modelling approach based on efficient material requirement planning. BIM-DAS was later tested using case study design and the results show that the major contributing factors to DfD are use of prefabricated assemblies and demountable connections. The results of the evaluation demonstrate the practicality of BIM-DAS as an indicator to measure the deconstructability of building designs. This could provide a design requirement benchmark for effective building deconstruction. This research work will benefit all stakeholders in the construction industry especially those interested in designing for deconstruction. The eventual incorporation of BIM-DAS into existing BIM software will provide a basis for the comparison of deconstructability of building models during design.

© 2015 Elsevier B.V. All rights reserved.Construction industry contributes a large portion of waste to landfill, which in turns results in environmental pollution and CO2 emission. Despite the adoption of several waste management strategies, waste reduction to landfill continues seeming an insurmountable challenge. This paper explores factors impeding the effectiveness of existing waste management strategies, as well as strategies for reducing waste intensiveness of the construction industry. Drawing on series of semi structured focus group discussions with experts from the UK leading construction companies, this paper combines phenomenological approach with a critical review and analysis of extant literatures. Five broad categories of factors and practices are responsible for ineffectiveness of construction and demolition waste management strategies, which subsequently results in waste intensiveness of the industry. These include end of pipe treatment of waste, externality and incompatibility of waste management tools with design tools, atomism of waste management strategies, perceived or unexpected high cost of waste management, and culture of waste behaviour within the industry. To reduce waste intensiveness of the construction industry, the study suggests that six factors are requisites. These are tackling of waste at design stage, whole life waste consideration, compliance of waste management solutions with BIM, cheaper cost of waste management practice, increased stringency of waste management legislation and fiscal policies, and research and enlightenment. The proposed strategies are not only important for achieving low waste construction projects, they are important for reducing waste intensiveness of the construction. Implementation of the suggested measures would drive waste management practices within the construction industry.

© 2016 Informa UK Limited, trading as Taylor & Francis Group.This study identifies evaluation criteria with the goal of appraising the performance of existing construction waste management tools and employing the results in the development of a holistic building information modelling (BIM) framework for construction waste management. Based on the literature, this paper identifies 32 construction waste management tools in five categories: (a) waste management plan templates and guides, (b) waste data collection and audit tools (c) waste quantification models, (d) waste prediction tools, and (e) geographic information system (GIS)-enabled waste tools. After reviewing these tools and conducting four focus-group interviews (FGIs), the findings revealed six categories of evaluation criteria (a) waste prediction; (b) waste data; (c) commercial and procurement; (d) BIM; (e) design; and (f) technological. The performance of the tools is assessed using the evaluation criteria and the result reveals that the existing tools are not robust enough to tackle construction waste management at the design stage. The paper therefore discusses the development of a holistic BIM framework with six layers: application; service domain; BIM business domain; presentation; data; and infrastructure. The BIM framework provides a holistic approach and organizes relevant knowledge required to tackle construction waste effectively at the design stage using an architecture-based layered approach. This framework will be of interest to software developers and BIM practitioners who seek to extend the functionalities of existing BIM software for construction waste management.

The significance of public sector guarantees in stimulating PFI/PPP development has considerably increased since the recent global financial crisis in 2007-08. A Major reason for this is the drastic decline in availability of bank lending, bonds and capital markets finances for long term PFI infrastructure projects. Despite the introduction of the UK Guarantee Scheme for Infrastructure in July 2012 which was designed to attract private investments for new public infrastructures, investors in PFI have only managed to guarantee few projects so far. The overall goal of this study explores the impact of the scheme on the industry, its challenges and critical parameters for securing approval for Government Guarantee in PFI projects. Using a methodology of phenomenology, literature review and unstructured interviews were conducted with construction contractors, public sector employees, equity investors and lenders in UK construction industry. The study discusses five critical parameters necessary for guaranteeing government approval in PFI projects. These factors include (1) project compliance with UK National Infrastructure Plan, (2) demonstration of project bankability and risk management, (3) certainty in planning approval, (4) prove of dependence on guarantee and (5) value for money to taxpayers. Evidently, in-depth understanding of these necessary criteria will enable industry players (equity sponsors, banks, contractors and subcontractors) access such guarantee and improve the bankability of prospective PFI/PPP Projects.

As a result of increasing recognition of effective site management as the strategic approach for achieving the required performance in construction projects, this study seeks to identify the key site management practices that are requisite for construction waste minimization. A mixed methods approach, involving field study and survey research were used as means of data collection. After confirmation of construct validity and reliability of scale, data analysis was carried out through a combination of Kruskal-Wallis test, descriptive statistics and exploratory factor analysis. The study suggests that site management functions could significantly reduce waste generation through strict adherence to project drawings, and by ensuring fewer or no design changes during construction process. Provision of waste skips for specific materials and maximisation of on-site reuse of materials are also found to be among the key factors for engendering waste minimization. The result of factor analysis suggests four factors underlying on-site waste management practices with 96.093% of total variance. These measures include contractual provisions for waste minimization, waste segregation, maximisation of materials reuse and effective logistic management. Strategies through which each of the underlying measures could be achieved are further discussed in the paper. Findings of this study would assist construction site managers and other site operatives in reducing waste generated by construction activities.

© Emerald Group Publishing Limited.Purpose - Competency-based measure is increasingly evident as an effective approach to tailoring training and development for organisational change and development. With design stage widely reckoned as being decisive for construction waste minimisation, the purpose of this paper is to identify designers' competencies for designing out waste. Design/methodology/approach - Due to paucity of research into competency for construction waste mitigation, this study corroborates verbal protocol analyses (VPA) with phenomenological research. Findings - Combining findings from the two methodological approaches, competencies for designing out waste are grouped into five categories, three of which are largely task related and two being contextual competencies. The study suggests that design task proficiency, low waste design skills and construction-related knowledge are indispensable task competencies, while behavioural competence and inter-professional collaborative abilities are requisite contextual competencies for designing out waste. In concurrence with task-contextual theory of job performance, personality variables and cognitive abilities are found to influence one another. This suggests that both task and contextual competencies are not only important, they are less mutually exclusive with respect to designing out waste. Practical implications - This study implies that apart from commitment and dedication of designers to waste minimisation, design and firm practices are expected to be adapted to the industry's standard. Originality/value - Basis for training needs of design professionals as well as redeployment criterion are further elaborated in the paper. By enhancing competencies identified in this study, construction waste would not only be significantly designed out, adequate cost saving could be made as a result of waste reduction.

© 2014 Elsevier B.V. All rights reserved.Construction industry consumes about half of all material resources taken from nature, and generates a large portion of waste to landfill. A way of tackling negative environmental impacts impending from continuous material extraction and waste generation is the use of recycled materials for construction projects. However, the use of recycled materials is yet to become a commonplace in construction industry. This study evaluates the factors hampering the use of recycled products in UK construction industry as well as strategies that could be adopted to enhance its use in the industry. In order to identify the impediments and critical strategies, a two-fold methodical approach was used. An unstructured interview preceded a quantitative questionnaire survey which was used to elicit broader industry practitioners' opinion. The study shows that designers rarely specify recycled products. This is due to lack of adequate information about quality and market availability of the products, negative perception from clients, and unexpectedly high cost of the products, despite its perceived low quality. The study suggests that a number of strategies could be adopted to promote the use of the products. These include allocation of points to the use of recycled products in sustainable design appraisal tools, governments legislative measures, improved collaboration between designers, contractors and materials suppliers, contractors involvement at earlier stage of design, improved education of the professionals about the products, and the use of tax break to influence the cost of the products. The findings of this study would therefore help policy makers, manufacturers and construction professionals to identify the factors hampering the use of recycled products for construction projects as well as the strategies that could be adopted in order to create market for the products.

This study aims to evaluate the extent to which building material specification affects life cycle environmental performance, using a building information modelling (BIM)-enhanced life cycle assessment (LCA) methodology. A combination of the BIM-based design and analysis tool Revit Architecture, the energy simulation tool Green Building Studio (GBS) and the LCA tool ATHENA Impact Estimator were used for the assessment. The LCA was carried out on a life case study of a 2100 m2 two-floor primary-school building, as well as a variability analysis, by varying the material specification in terms of whole building materials. The life cycle performance of the buildings was primarily evaluated in terms of its global warming potential (GWP) and health impact. The findings of the study show that irrespective of the materials used, buildings that are based on renewable energy perform better than those based on fossil fuels over their life cycle. In terms of building materials, both environmental and health preferences of buildings congruently range from timber, brick/block and steel to insulated concrete formwork (ICF), in descending order. The study suggests that as buildings become more energy efficient during operational stages, serious attention needs to be given to their embodied impact. The study lays out a methodological framework that could be adopted by industry practitioners in evaluating life cycle environmental impact of different BIM-modelled material options at the building conception stage. This has the tendency to ensure that the highest proportion of life cycle environmentally beneficial material combinations are selected during specification and construction.

The overall aim of this study is to investigate the potential of Building Information Modelling (BIM) for construction waste minimization. We evaluated the leading BIM design software products and concluded that none of them currently support construction waste minimization. This motivates the development of a plug-in for predicting and minimizing construction waste. After a rigorous literature review and conducting four focused group interviews (FGIs), 12 imperative BIM factors were identified that should be considered for predicting and designing out construction waste. These factors were categorized into four layers, namely the BIM core features layer, the BIM auxiliary features layer, the waste management criteria layer, and the application layer. Further, a process to carry out BIM-enabled building waste analysis (BWA) is proposed. We have also investigated the usage of big data technologies in the context of waste minimization. We highlight that big data technologies are inherently suitable for BIM due to their support of storing and processing large datasets. In particular, the use of graph-based representation, analysis, and visualization can be employed for advancing the state of the art in BIM technology for construction waste minimization.

The aim of this paper is to identify Critical Success Factors (CSF) needed for effective material recovery through Design for Deconstruction (DfD). The research approach employed in this paper is based on a sequential exploratory mixed method strategy. After a thorough review of literature and conducting four Focus Group Discussion (FGDs), 43 DfD factors were identified and put together in a questionnaire survey. Data analyses include Cronbach's alpha reliability analysis, mean testing using significance index, and exploratory factor analysis. The result of the factor analysis reveals that an underlying factor structure of five DfD factors groups that include ‘stringent legislation and policy’, ‘deconstruction design process and competencies’, ‘design for material recovery’, ‘design for material reuse’, and ‘design for building flexibility’. These groups of DfD factor groups show that the requirements for DfD goes beyond technical competencies and that non-technical factors such as stringent legislation and policy and design process and competency for deconstruction are key in designing deconstructable buildings. Paying attention to the factors identified in all of these categories will help to tackle impediments that could hinder the effectiveness of DfD. The results of this study would help design and project managers to understand areas of possible improvement in employing DfD as a strategy for diverting waste from landfills.

Purpose: There have been speculations as to whether environmental friendly buildings are always healthy. Using lifecycle assessment (LCA) methodology, the purpose of this paper is to investigate lifecycle relationship between building sustainability and its environmental health impacts Design/methodology/approach: In order to achieve this, a block of classroom was modelled with the aid of Revit software, and its lifecycle global warming potential (GWP) and human health impacts were analysed using green building studio and ATHENA impact estimator tools. Sensitivity analyses of the block of classrooms were then carried out by varying the building materials and energy use pattern of the original typology. The LCA was performed for seven alternative typologies that were achieved through variation in the building materials and energy use patterns. Findings: For all the eight building typologies, the study shows a direct relationship between GWPs and human health impacts. This confirms that the more sustainable a building, the less its tendency for having negative health effects on building operatives, occupants and the wider environment. Again, the more green a building in terms of its materials and energy use pattern, the healthier the building becomes. Research limitations/implications: The human health impacts was evaluated by measuring amount of particulate matter (PM2.5) produced by the buildings while environmental impact was evaluated by measuring global warming (KgCO2) potentials of the buildings throughout its lifecycle. The study has been based on the impacts of building materials and energy use patterns over the entire lifecycle of the buildings and materials used for construction. Originality/value: The study established a positive relationship between GWP of building and its human health impacts. Thus, all arguments relating to the relationship between building sustainability and health are laid to rest by the paper.

Construction industry insolvency studies have failed to stem the industry’s high insolvency tide because many focus on big civil engineering firms (CEF) when over 90% firms in the industry are small or micro (S&M). This study thus set out to uncover insolvency criteria of S&M CEFs and the underlying factors using mixed methods. Using convenience sampling, storytelling method was used to execute interviews of 16 respondents from insolvent firms. Narrative and thematic analysis were used to extract 17 criteria under 2 groups. Criteria were used to formulate questionnaire of which 81 completed copies were received and analysed using Cronbach’s alpha coefficient and relevance index score for reliability and ranking respectively. The five most relevant criteria are: economic recession, immigration, too many new firms springing up, collecting receivables and burden of sustainable construction. The 4 underlying factors established through factor analysis are: market forces, competence-based management, operations efficiency and other management issues and information management. The factors were in line with Mintzberg’s and Porters’ strategy theories. Results demonstrate that insolvency factors affecting big and small CEF can be quite different and sometimes, even opposite. This research will provide a unique resource on the ‘beware’ factors for potential owners of S&M CEF. The criteria are potential variables for insolvency prediction models for S&M CEFs.

Apart from various fiscal and legislative measures, several research efforts have been made towards ensuring adequate diversion of construction waste from landfill. However, despite these joint efforts, waste landfilling continues being a popular waste management approach, suggesting ineffectiveness of the existing waste management strategies. As such, this study seeks to identify knowledge gap and suggest holistic solution for effective construction waste management. The overall methodological approach of the paper was a critical review and analysis of extant literatures, coupled with series of focus group interview with construction professionals. Strong indications emerged from the study that the reason for ineffectiveness of the existing waste management strategies is due to their BIM incompatibility, provision of solution after waste has occurred and evaluation of waste and construction activities from static perspective. Owing to these, this paper propose a holistic dynamic BIM system which considers all stages of building delivery processes, employ dynamic system approach, and interoperable with design, procurement and construction management tools and platforms. As such, the study argues that for waste management approaches to effectively minimize construction waste, it must consider dynamism of construction activities, be BIM compliant, and should consider interrelationship between design, procurement and construction stages. The study is expected to furnish the construction industry practitioners, policy makers and researchers with the tactics lacking in existing waste management strategies, thereby enhancing adequate waste diversion from landfill.

Various physical, chemical and biological hazards that affect human health arise in the built environment. There is need for more awareness by both the designers and building occupants, so that necessary preventive measures would be incorporated in the design of new builds, and proper remedies would be applied in case of dealing with existing ones. Consequently, this paper identifies causative factors of building related health issues and explores their likely health and safety impacts. It also evaluates the nature, efficiency and effectiveness of mitigations placed on each of the likely hazards in the United Kingdom and Australia, as case studies. Various design and construction techniques through which some of the hazards could be mitigated, as well as the strategies which building occupants can adopt in order to prevent, or in worst case scenario minimize, the effects of such hazards are also evaluated. While the nations used as case study scenarios already have some improvable legislative measures in tackling building health issues, it is expected that every nation follow suit and provide legislative measures capable of preventing building related health issues.

© 2015 IEEE.Bankruptcy prediction models (BPMs) are needed by financiers like banks in order to check the credit worthiness of companies. A very robust model needs a very large amount of data with periodic updates (i.e. appending new data). Such size of data cannot be processed directly by the tools used in building BPMs, however Big Data Analytics offers the opportunity to analyse such data. With data sources like DataStream, FAME, Company House, etc. that hold large financial data of existing and failed firms, it is possible to extract huge financial data into Hadoop database (e.g. HBase), whilst allowing periodic appending of data from the data sources, and carry out a Big Data analysis using a machine learning tool on Apache Mahout. Lifelong machine learning can also be employed in order to avoid repeated intensive training of the model using all the data in the Hadoop database. A framework is thus proposed for developing a Big Data Analytics based BPM.

While many studies are still focusing on sustainability in construction in terms of greening or resource consumption reduction, the construction industry (CI) has been struggling more with the sustainability of construction firms in terms of survival. For example, more than 1,500 construction firms went bust at the start of 2012 alone, with the industry leading the liquidated companies in England and Wales chart at the end of 2013. Despite many studies into the causes of failure of firms, the firms in the CI have continued to perform poorly. On the world scale, the CI has consistently been the second worst industry in terms of corporate failure. The objective of this study is hence to identify the key factors that determine/affect the sustainability of a construction enterprise. The methodology used is mainly phenomenology. Phenomenology, otherwise known as interpretivism, is known for its suitability for discovering root causes of events comprehensively. Ten comprehensive unstructured interviews were carried out to identify the factors using convenience and snowballing sample methods. The target respondents were mainly managerial level employees, directors and owners of failed construction firms. Target construction firms were only small and medium enterprise (SMEs). The interviews were analysed qualitatively using Nvivo. The results showed that cash flow; investment in operation/assets; immigration; over reliance on certain source(s) for project; multiple responsibilities; lack of construction business knowledge; bid pricing; and firm’s age are the most important factors affecting the sustainability of construction firms.

© 2016 Elsevier LtdThe ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon—dubbed as Big Data—has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data; which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature, investigating the application of Big Data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of Big Data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of Big Data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and Big Data Analytics in the context of the construction industry. We discuss the current state of adoption of Big Data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with Big Data adoption in the industry.

Owing to its contribution of largest portion of landfill wastes and consumption of about half of mineral resources excavated from nature, construction industry has remained a major target for achieving global sustainability. Despite an adoption of several waste management strategies, and introduction of various legislative measures, reducing waste generated by the industry remains challenging. In order to understand cultural factors contributing to waste intensiveness of the industry, as well as those preventing effectiveness of existing waste management strategies, this study examines cultural profile of construction industry. Drawing on four focus group discussions with industry experts, the study employs phenomenological approach to explore waste inducing culture. Combining findings from phenomenological research with extant literatures, the study suggests that in order to reduce waste intensiveness of the construction industry, four waste inducing cultures need to be changed. These include (i) make-do understanding that usually result in make-do waste (ii) non-collaborative culture, which results in reworks and other forms of wasteful activities (iii) blame culture, which encourages shifting of waste preventive responsibilities between designers and contractors, and (iv) culture of waste behaviour, which encourages belief in waste inevitability. These sets of measures are not only important for engendering waste management practices; they are requisite for effectiveness of existing strategies. Thus, this paper advocates cultural shift as a means of enhancing sustainability and profitability of the construction industry.

Performance of bankruptcy prediction models (BPM), which partly depends on the methodological approach used to develop it, has virtually stagnated over the years. The methodological positions of BPM studies were thus investigated. Systematic review was used to search and retrieve 70 journal articles and doctoral theses. Their “general methods” and “philosophical underpinnings” were investigated using summary of findings tables and meta-analysis. “General methods” results showed positive trends in terms of techniques being used, error cost consideration and model validation, with some use of skewed data being the main drawback. For “philosophical underpinnings”, positivism paradigm was discovered to be at the core of BPM studies. This is deemed inadequate because of the need to consider industries’ dynamism, financial variables flaws and social factors which actually lead to the financial status of firms. The pragmatism paradigm using mixed method is proposed. A research design framework for executing the proposed methodology is presented. This will help BPM developers go through more rigorous and robust methodology to deliver better and more valid models. Limitations of study include not reviewing studies not reported in English language and impact of different countries’ accounting practices on ratios. Limited availability of theses’ database resulted in reviewing only four theses.

Although construction waste occurs during the actual construction activities, there is an understanding that it is caused by activities and actions at design, materials procurement and construction stages of project delivery processes. This study investigates the material procurement and logistics measures for mitigating waste generated by construction activities. In a bid to explore the phenomenon from the perspectives of experts from the construction industry, this study used a combination of descriptive interpretive research and survey approach as its methodological framework. The study suggests that four features characterised waste efficient logistic and procurement process. These include suppliers' commitment to low waste measures, low waste purchase management, effective materials delivery management and waste efficient Bill of Quantity. In addition, the key requisite strategies for mitigating construction waste through materials procurement include commitment to take back scheme , procurement of waste efficient materials/technology and use of minimal packaging. The use of Just-in-Time (JIT) delivery system and prevention of over ordering are also important for mitigating waste through materials procurement processes. These implies that while the key measures are critical success factors for reducing waste through procurement process, the four established features are required of all procurement process. Measures through which the procurement process could enhance waste efficiency are further highlighted and discussed in the paper. Findings of this study could assist in understanding a set of measures that should be taken during materials procurement process, thereby corroborating waste management practices at design and construction stages of project delivery process.

Predicting and designing out construction waste in real time is complex during building waste analysis (BWA) since it involves a large number of analyses for investigating multiple waste-efficient design strategies. These analyses require highly specific data of materials that are scattered across different data sources. A repository that facilitates applications in gaining seamless access to relatively large and distributed data sources of building materials is currently unavailable for conducting the BWA. Such a repository is the first step to developing a simulation tool for the BWA. Existing product data exchange ontologies and classification systems lack adequate modelling of building materials for the BWA. In this paper, we propose a highly resilient and data-agnostic building materials database. We use ontologies at the core of our approach to capture highly accurate and semantically conflicting data of building materials using the Resource Description Framework (RDF) and Web Ontology Language (OWL). Owing to the inherent capabilities of RDF, the architecture provides syntactical homogeneity while accessing the diverse and distributed data of building materials during the BWA. We use software packages such as Protégé and Oracle RDF Graph database for implementing the proposed architecture. Our research provides technical details and insights for researchers and software engineers who are seeking to develop the semantic repositories of similar kind of simulation applications that can be used for building waste performance analysis.

Legislation and fiscal policies have remained the key drivers of construction waste minimization. It has often been suggested that reducing waste to the landfill does not only require improvement on existing waste management policies and fiscal framework; there is a need for adequate inputs from the construction professionals. As a means of engendering effective waste management policies, this study explores industry practitioners’ viewpoints on effective policies for minimising waste landfilled by the UK construction industry. Using exploratory sequential mixed method approach, qualitative and quantitative methods were used. In the first phase of the study, data was collected through focus group discussions with 24 experts from the UK construction industry. Findings from the qualitative study served as an input into a questionnaire, which was used to elicit a wider opinion from 63 experts at the quantitative stage of the study. The study suggests that for waste management legislation and policies to effectively drive construction waste minimization, six key measures are important. These include (i) provision of tax breaks and incentives to good waste performers and waste management businesses; (ii) increased targeting of design stages in policies; (iii) Extension of sustainable design appraisal systems by allocating more points to proven waste performance measures; (iv) increased stringency of legislative measures by requiring use of proven waste efficient design, procurement and construction methods; (v) increased stringency of fiscal policies by increasing penalties for poor waste performance; and (vi) corroboration of policy requirements with enablers and facilitators. The strategies through which each of the legislative and fiscal measures could be tailored and enhanced are discussed in the paper. By embracing both stringent and palliative policy measures suggested in the study, substantial construction waste could be diverted from landfill.

Despite the consensus that waste efficient design is important for reducing waste generated by construction and demolition activities, design strategies for actual waste mitigation remain unclear. In addition, decisive roles required of designers in designing out waste remains inadequately addressed. As such, this study aims to map out attributes of waste effective design and design documents. Drawing on series of semi-structured focus group discussions with experts from the UK leading design and construction companies, this paper employs qualitative approach to explore design and design document qualities for waste efficient construction projects. The study suggests that for a design to assist in reducing construction and demolition waste, it needs to fulfill five key requisites, while its documentation is expected to fulfill four key requisites. A waste efficient design would incorporate standardization and dimensional coordination, employ principles in modern methods of construction, provides measures for spatial and components flexibility, make provisions for end of life deconstruction and employs techniques in BIM for design coordination. Waste efficient design documentation, on the other hand, is characterised by completeness and clarity, certainty and timeliness, freedom from error, and incorporation of set of plans and schedules that are waste militating. A validation of these findings in a case study of waste-to-energy project confirmed that the strategies are essential to preventing construction waste. Measures through which design and design documents could achieve the identified waste effective attributes are highlighted and discussed. Findings of this study could assist in understanding a set of measures that should be taken at project planning and design stages in order to mitigate waste intensiveness of the construction industry. It would as well assist designers in understanding a set of attributes that must be possessed by design and design documents in order to design out construction waste.

While traditional construction has advanced its course over time, gathering innumerable data in improving its process, the traditional method at its best has been heavily faulted as not being able to address eminent and exigent global needs like shelter for all and emerging challenges relating to construction works like carbon emission ultimately resulting in global environmental sustainability challenges starring at our globe in its face, e.g. meeting net-zero carbon target, reducing global shelter deficits, etc. Despite the many advancements attempted to improve traditional buildings, its limitations keep calling for alternative means of housing provision and construction processes. Extant studies have identified Offsite construction (OSC) as an alternative to addressing the shortcomings of traditional construction methods. OSC, on the other hand, has the potential to address the shortcomings of traditional construction approaches in addressing technological advancements and global challenges, is a re-emerging solution whose process is yet to be fully articulated. OSC, however, does not have a project database with which to run. This study appraised extant literature for issues bothering OSC data availability, collation and management and, therefore, aims to provide a framework for OSC data collation to develop a robust OSC data repository for the UK OSC in driving the wider OSC adoption. The framework presented the minimum valuable products (MVPs) required for real-time data gathering of the various issues influencing OSC adoption and holds the potential to provide academic scholars, industry captains and policymakers with adoption intelligence needed to drive wider OSC adoption.

Traditional construction methods in the UK have struggled to address the industry’s evolving challenges. In response, there has been a concerted effort to increase the adoption of Offsite Construction (OSC), a factory-based production approach that offers a viable alternative to traditional methods. However, the resurgence of OSC has elicited diverse reactions within and outside the industry. Research highlights the significance of human response as a critical factor in the adoption process. This study, following an extensive literature review, conducted qualitative interviews with twelve UK construction professionals to explore industry and public reactions to OSC and identify the behavioural challenges affecting its broader adoption. The analysis revealed that perception and attitude are major obstacles, although there is a noted willingness within the industry to trial OSC. This study enhances the understanding of these attitudinal issues, offering valuable insights for scholars, practitioners, and policymakers aiming to increase OSC uptake.

Rapid urbanization and population growth in recent decades have placed significant pressure on urban cities to rely heavily on underground infrastructure, such as sewers and tunnels, to maintain the provision of essential services. These sewers, typically having a limited lifespan of 50 to 100 years, are prone to various forms of defects. While prior research has primarily addressed common sewer defect like crack, root intrusion, and infiltration among others, the challenge of encrustation—the formation of hard deposits within sewer systems—has received less attention. This study presents a pioneering deep-learning approach to detect encrustation in sewers by leveraging survey videos from 14 different sewers in the United Kingdom. Our work marks the first effort to develop models specifically for detecting encrustation using deep learning techniques, as previous studies have focused on other types of deposits such as settled and attached deposits. By converting the videos into sequential image frames, we subjected them to thorough analysis and several image pre-processing techniques. Our contributions include the development and comparison of different classification models using backbone CNN networks such as AlexNet, VGG16, EfficientNet, and VGG19 to classify encrustation. Notably, this study provides the first metric-based comparison of these backbone networks to identify the most effective model for encrustation detection. The results demonstrate an impressive 96 % accuracy using the deep architecture of VGG19. Beyond accuracy, this research explores the impact of data augmentation and network dropout on reducing overfitting and enhancing model performance. Additionally, we analyze the time complexities associated with training models with and without data augmentation, providing valuable insights into the efficiency of our approach.

Purpose This study aims to develop a comprehensive conceptual framework that serves as a foundation for identifying most critical delay risk drivers for Building Information Modelling (BIM)-based construction projects. Design/methodology/approach A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify key delay risk drivers in BIM-based construction projects that have significant impact on the performance of delay risk predictive modelling techniques. Findings The results show that contractor related driver and external related driver are the most important delay driver categories to be considered when developing delay risk predictive models for BIM-based construction projects. Originality/value This study contributes to the body of knowledge by filling the gap in lack of a conceptual framework for selecting key delay risk drivers for BIM-based construction projects, which has hampered scientific progress toward development of extremely effective delay risk predictive models for BIM-based construction projects. Furthermore, this study's analyses further confirmed a positive effect of BIM on construction project delay.

In various cities/other urban settlements, buildings are replaced with newer stocks, ending many buildings' lives. Unfortunately, these buildings nearing or at end-of-useful lives are mostly not deconstructed; instead, they get demolished, resulting in waste generation and pollution, among other environmental concerns. Deconstruction supports closing the material loop in construction, facilitating reuse at end-of-life of the building; however, it is not always easy to assess the feasibility of deconstruction for existing buildings – deconstructability. For this purpose, this paper investigated critical factors that needed to be checked to make informed decisions about the deconstructability of buildings. These factors cover economic, social, environmental, legal, and technical dimensions. Based on the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), 31 significant drivers were identified. These drivers were classed into seven factors. The findings in this paper contribute to the practice of deconstruction, mainly supporting deconstructability decision-making and are helpful for deconstruction/demolition auditors, waste-management consultants and/or other stakeholders with waste minimisation goals, particularly for buildings nearing or at the end-of-useful lives.

The application of statistical and artificial intelligence (AI) tools in building energy prediction (BEP) is considered one of the most effective advances toward improving energy efficiency. Thus, researchers are constantly propagating the energy prediction field with many prediction models using diverse statistical and AI tools. However, many of these tools are employed in unsuitable data conditions or for wrong situations. Using the Institute of Electrical and Electronics Engineers and Scopus databases, 92 journal articles on statistical and AI tools in BEP were systematically analyzed. Furthermore, a quantitative bibliometric analysis was conducted to pinpoint the trends and examine knowledge gaps. This research reviews the performance of nine popular and promising statistical and AI tools with a primary focus on seven pertinent criteria within the building energy research domain. Although it was concluded that no one tool is best in all criteria, a diagrammatic framework is provided to serve as a guide for appropriate tool selection in various situations. This study contributes to appropriate tool selection in the development of BEP models and their related drawbacks. In addition, this study also evaluated the performance of the high-performing tools on a standard dataset.

Purpose: The construction industry has embraced Building Information Modelling (BIM) as a practical methodology towards digitalisation. However, critics believe BIM has reached a plateau in addressing distinctive construction challenges. As a result, literature has seen an increase in the development and use of BIM-based plugins. Overall, the plugins have been critical in delivering custom-built solutions to longstanding construction challenges. The objective of this study is to empirically investigate this trend and the potential barriers undermining the wider development of BIM plugins, by that contributing to a stimulating research topic and a growing knowledge gap. Design/methodology/approach: Methodologically, the study utilised a quantitative approach to collect data through a carefully designed questionnaire. The study achieved a sample size of 39 experts who have been involved in developing, experimenting, and publishing BIM-based plugins for specific construction activities. Findings: The findings led to the identification of the key barriers to using BIM-based plugins and the identification of the key strategies to overcome them across the three Situational Awareness (SA) phases. Results also suggest that the development and use of plugins are destined to increase, and the research community can now rely on the insights of this paper as a departure point to address the technological plateau in BIM-related research. Originality/value: This is the first study to empirically identify and assess the barriers undermining the wider development of BIM-based plugins. The study contributes to theory by building on SA, by challenging existing wisdom and fostering new knowledge around strategies to overcome the evaluated barriers.

Purpose The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data. Design/methodology/approach For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model. Findings Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM2.5 concentration level than bagging and boosting ensemble models. Research limitations/implications A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast. Practical implications The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system Originality/value This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways to understand building energy efficiency. In several studies, various machine learning models have been proposed for the prediction of building energy efficiency. However, the existing models are based on classical machine learning approaches and small datasets. Using a small dataset and inefficient models may lead to poor generalization. In addition, it is not common to see studies examining the suitability of machine learning methods for forecasting the energy consumption of buildings during the early design phase so that more energy-efficient buildings can be constructed. Hence, for these purposes, we propose a multilayer extreme learning machine (MLELM) for the prediction of annual building energy consumption. Our MLELM fuses stacks of autoencoders (AEs) with an extreme learning machine (ELM). We designed the autoencoder based on the ELM concept, and it is used for feature extraction. Moreover, the autoencoders were trained in a layer-wise manner, employed to extract efficient features from the input data, and the extreme learning machine model was trained using the least squares technique for a fast learning speed. In addition, the ELM was used for decision making. In this research, we used a large dataset of residential buildings to capture various building sizes. We compared the proposed MLELM with other machine learning models commonly used for predicting building energy consumption. From the results, we validated that the proposed MLELM outperformed other comparison methods commonly used in building energy consumption prediction. From several experiments in this study, the proposed MLELM was identified as the most efficient predictive model for energy use before construction, which can be used to make informed decisions about, manage, and optimize building design before construction.

Deconstruction is an innovative and sustainable option for building end-of-life. It can turn the negative impacts of demolition, including diverting valuable resources from the congested landfill into beneficial use through reuse and recycling. However, the feasibility of deconstruction has placed a massive limitation on the implementation of deconstruction. This research carried out a systematic literature review of 35 academic and 3 non-academic pieces of literature to develop a construct-based deconstructability framework. This framework – built around technical, economic, legal, operational, schedule and social construct – describes the condition under which deconstruction is likely to work and drivers influencing deconstructability. A total of 44 drivers influencing deconstructability were established and ranked from which design and building technology, cost including expense and revenues from the resale, supply and demand of the recovered component and material, the schedule for the deconstruction were identified as most influential. However, every identified driver should be considered during the deconstructability assessment of a building.

Purpose: This study investigates the requisite measures for enhancing sustainability practices within the Indonesian construction industry based on professional perspectives. Design/Methodology/Approach: This study used quantitative approach for data collection. A survey was conducted, using a questionnaire completed by 482 Indonesian construction professionals as a research instrument, and data were analyzed through reliability and exploratory factor analysis. Findings: The findings suggest that for sustainability practices to become widely implemented within the Indonesian construction industry, certain measures are required. These include the need to raise awareness through education, development of standardized benchmarks, and the implementation of appraisal systems by the government, professional bodies, and academic institutions. These approaches are expected to build capacity and enhance the knowledge of sustainability among construction professionals and their clients. In addition, sustainable construction practices could be encouraged through a subsidized green market, which could be finalized supported by the government from financial penalties levied against non-sustainable practices. Originality/Value: By implementing the underlying strategies within the Indonesian construction industry, the poor knowledge, awareness, implementation, and motivation for sustainable practices within the Indonesian construction industry could be addressed, thereby reducing the environmental impacts associated with buildings and construction activities.

Purpose This paper examines the categories of property attributes that are important to UK university students in their online accommodation search. It also analyses the volume of information displayed regarding the property attributes and explores the influence of some of the information provided on the attractiveness and by extension, the booking potential of the property. Design/methodology/approach The authors use data from an online student accommodation listing platform – student.com – which contain tangible and non-tangible property attributes, and the data are analysed using a hedonic regression model. Findings The results show that purpose-built student accommodation's (PBSA) tangible and intangible attributes are important to students in their online accommodation search, although, these attributes vary in impact. The study also reveals that failure to display key information of a PBSA may reduce the attractiveness of the property. Originality/value The empirical evidence on student accommodation ex ante preferences and choices is limited, particularly as it relates to online accommodation search in a UK context. The authors' approach to identify the application of the search theory to the student accommodation search process is particularly unique.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic datasets on air quality predictions has not been clearly investigated. This research investigates the effects traffic dataset have on the performance of Machine Learning (ML) predictive models in air quality prediction. To achieve this, we have set up an experiment with the control dataset having only the Air Quality (AQ) dataset and Meteorological (Met) dataset. While the experimental dataset is made up of the AQ dataset, Met dataset and Traffic dataset. Several ML models (such as Extra Trees Regressor, eXtreme Gradient Boosting Regressor, Random Forest Regressor, K-Neighbors Regressor, and five others) were trained, tested, and compared on these individual combinations of datasets to predict the volume of PM2.5, PM10, NO2, and O3 in the atmosphere at various time of the day. The result obtained showed that various ML algorithms react differently to the traffic dataset despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%. This research is limited in terms of the study area and the result cannot be generalized outside of the UK as many conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research. Therefore, leaving out a few other ML algorithms. This study reinforces the belief that the traffic dataset has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form traffic dataset in the development of an air quality prediction model. This implies that developers and researchers in air quality prediction need to identify the ML algorithms that behave in their best interest before implementation. This will enable researchers to focus more on algorithms of benefit when using traffic datasets in air quality prediction.

Procurement decision-making is a crucial determinant of project success. Although several objective, stage-based models have been proposed to guide clients’ procurement choices, little emphasis has been made on the subjective nature of construction clients. Recognizing the role of clients’ experiences in justifying procurement routes, this study develops a decision-making framework that is capable of guiding construction clients in making informed procurement choices. Adopting a mixed-method approach, comprising semi-structured interviews and multi-objective optimization, relevant procurement options were appraised based on clients’ specifications and project deliverables. The lived experiences of construction clients and the importance they attach to pre-defined selection rating criteria were subsequently evaluated, using a template that enables clients to prioritize procurement methods for different project types. The resultant framework offers a holistic, practical, and collaborative procurement selection process that promotes the efficient delivery of construction projects by reducing the cost overrun and delays associated with uninformed client decisions in construction procurement.

The adoption of sustainable practices is essential to mitigating negative impacts associated with the global construction industry. This study investigates barriers inhibiting the transition to sustainable practices in the Indonesian construction industry. Using a questionnaire as a means of data collection, relevant data were analyzed using reliability and exploratory factor analysis. Based on the analysis, the findings suggest that there were eight underlying factors responsible for the poor awareness of sustainability and the current low level of sustainable construction practices in the Indonesian construction industry. The three most significant barriers toward sustainable practices in Indonesia are lack of knowledge and standards, poor design practices, and financial constraints. The sustainable standards practice in Indonesia is still in its infancy and immature compared to developed countries. The findings of this study are expected to provide guidance and knowledge to construction players related to barriers in sustainability practices within the Indonesian construction industry.

The construction industry produces enormous volumes of construction waste that have a negative influence on the environment. This study examines the underlying causes of waste generation in the Indonesian construction industry based on professional perspectives. This study used a questionnaire as a research instrument and examined data with reliability and exploratory factor analysis. Based on the analysis, there are eight underlying causes that contribute to the generation of construction waste in the Indonesian construction sector. The five most significant underlying causes of waste generation that obstruct sustainable practices are waste-inducing site and human resources management approach, inadequate collaboration and support among stakeholders, equipment management approach, material logistics management, and poor working environment. As a result, it is crucial for Indonesian experts to pay attention to the factors that contribute to waste generation in order to lessen its negative effects on the environment and promote sustainable practices.

This study discusses the future directions of effective Design for Deconstruction (DfD) using BIM-based approach to design coordination. After a review of extant literatures on existing DfD practices and tools, it became evident that none of the tools is BIM compliant and that BIM implementation has been ignored for end-of-life activities. To understand how BIM could be employed for DfD and to identify essential functionalities for a BIM-based deconstruction tool, Focus Group Interviews (FGIs) were conducted with professionals who have utilised BIM on their projects. The interview transcripts of the FGIs were analysed using descriptive interpretive analysis to identify common themes based on the experiences of the participants. The themes highlight functionalities of BIM in driving effective DfD process, which include improved collaboration among stakeholders, visualisation of deconstruction process, identification of recoverable materials, deconstruction plan development, performance analysis and simulation of end-of-life alternatives, improved building lifecycle management, and interoperability with existing BIM software. The results provide the needed technological support for developing tools for BIM compliant DfD tools.

In an attempt to stem the tide of mass failure of construction businesses, or support financiers and clients in identifying healthy construction firms for loans and contracts respectively, many researchers have developed construction industry bankruptcy prediction models (CI-BPMs). The effectiveness of such CI-BPMs is partly dependent on the methodology used in building it. Despite the usual claim of high accuracy by developers/authors, none of the CI-BPMs developed has gained wide acceptance in the construction industry, leading to development of new ones in succession. This study hence reviews the methodological positions in CI-BPM studies using the complete available population. After a critical content analysis of the features of CI-BPM studies, they all appeared to have used the positivism paradigm with realist ontology, objective epistemology and deductive approach. Although the main aim of CIBPM studies to ‘predict’ failure, an action (i.e. prediction) which is well ingrained in the positivism paradigm, makes the generally adopted positivism paradigm appear very appropriate, the aggressive dynamism of the construction industry and the experts’ criticism of the methodology clearly makes it inappropriate. This work proposes pragmatism, in the methodological pluralism form, as the best paradigm for CI-BPM research with realist ontology, combined subjective and objective epistemology, mixed-method research choice, case study, archival and survey strategies, and the deductive research approach. A complete research design framework for executing the proposed methodology is presented.

Albeit the understanding that construction waste is caused by activities ranging from all stages of project delivery process, research efforts have been concentrated on design and construction stages, while the possibility of reducing waste through materials procurement process is widely neglected. This study aims at exploring and confirming strategies for achieving waste-efficient materials procurement in construction activities. The study employs sequential exploratory mixed method approach as its methodological framework, using focus group discussion, statistical analysis and structural equation modelling. The study suggests that for materials procurement to enhance waste minimisation in construction projects, the procurement process would be characterised by four features. These include suppliers’ commitment to low waste measures, low waste purchase management, effective materials delivery management and waste-efficient Bill of Quantity, all of which have significant impacts on waste minimisation. This implies that commitment of materials suppliers to such measures as take back scheme and flexibility in supplying small materials quantity, among others, are expected of materials procurement. While low waste purchase management stipulates the need for such measures as reduced packaging and consideration of pre-assembled/pre-cut materials, efficient delivery management entails effective delivery and storage system as well as adequate protection of materials during the delivery process, among others. Waste-efficient specification and bill of quantity, on the other hand, requires accurate materials take-off and ordering of materials based on accurately prepared design documents and bill of quantity. Findings of this study could assist in understanding a set of measures that should be taken during materials procurement process, thereby corroborating waste management practices at other stages of project delivery process.

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers

Construction project manager (CPM) competency has been common place discussions over the years with the recognition of the importance of skills and knowledge to project success. With recent advancements in the application of digital technologies such as Building Information Modelling (BIM), it is imperative that CPMs develop new competencies that enable effective delivery of projects. The lack of clarity of the specific competencies needed by CPMs to deliver BIM-enabled projects, makes it important that the competencies required by CPMs for effective BIM-enabled project delivery be ascertained, such that this study addresses this gap. A sequential exploratory mixed method study is adopted to identify these competencies needed to effectively deliver BIM projects as well as validate the competencies of great value to BIM project delivery. Six key competency groups were identified as pertinent to BIM project delivery namely: Managerial and administrative; Strategic; Functional and operational; Leadership and people management; Technical and implementation; and Innate qualities. Overall, managerial and administrative competencies as well as leadership and people management competencies were regarded as being more crucial to the role of the CPM in BIM-enabled project delivery. This study provides useful guidance for development of training programmes as well as performance assessment systems for CPMs in the era of BIM based on the prioritisation of areas of competence.

Notwithstanding that efforts made at the design stage of building construction projects have significant impacts on project outcome, most waste management efforts are usually focussed on construction stage. This is albeit the understanding that construction waste could be significantly reduced through design activities. This study investigates the underlying design measures that are capable of minimising waste generated by construction and demolition activities. Using exploratory sequential mixed method research, the study employs focus group discussions and thematic analysis at the exploratory phase, while questionnaire and structural equation modelling were used at the explanatory stage of the study. The study suggests that construction waste could be significantly reduced by designing for standard materials size and by designing for modern method of construction, thereby minimising waste due to breakage, materials leftover and other major causes of waste. The study further suggests that the design process and design documentation are key underlying measures for mitigating waste in construction projects. This could be enhanced through such critical success factors as a collaborative delivery process, which is characterised by early involvement of contractors and adequate coordination of design between various disciplines involved. Findings of this study would assist designers and other construction stakeholders in understanding the significant measures for designing out waste in construction projects.

Notwithstanding the significant number of studies and other interventions, such as government policies and fiscal measures to address its waste intensiveness, the construction industry remains the largest contributor of wastes to landfill across many nations, while also consuming a significant proportion of energy and mineral resources. Consequently, this study aims at establishing the most effective strategies for mitigating waste during construction processes. The study adopts an exploratory sequential mixed-method approach, using the findings from thematically analysed focus group discussion and systematic literature review, from the exploratory stage to inform a questionnaire design. A Structural Equation Model (SEM) approach for Confirmatory Factor Analysis (CFA) was developed at the second stage of the study. The study suggests that while policy and legislative provisions should provide great motivation for construction waste mitigation, six key measures are requisite for effective waste mitigation. Most importantly, the use of prefabrication construction method instead of cast-in-situ could reduce construction waste significantly. Other key strategies include contractual provisions for waste minimization, maximization of materials reuse, contractors' dedication and competencies, waste effective site planning, and enhanced collaborative culture in project delivery. The findings of this study would assist in understanding the major measures and requisite practices for engendering waste minimization during the construction stage of project delivery processes.

The high proportion of energy consumed in buildings has engendered the manifestation of many environmental problems which deploy adverse impacts on the existence of mankind. The prediction of building energy use is essentially proclaimed to be a method for energy conservation and improved decision-making towards decreasing energy usage. Also, the construction of energy efficient buildings will aid the reduction of total energy consumed in newly constructed buildings. Machine Learning (ML) method is recognised as the best suited approach for producing desired outcomes in prediction task. Hence, in several studies, ML has been applied in the field of energy consumption of operational building. However, there are not many studies investigating the suitability of ML methods for forecasting the potential building energy consumption at the early design phase to reduce the construction of more energy inefficient buildings. To address this gap, this paper presents the utilization of several machine learning techniques namely Artificial Neural Network (ANN), Gradient Boosting (GB), Deep Neural Network (DNN), Random Forest (RF), Stacking, K Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision tree (DT) and Linear Regression (LR) for predicting annual building energy consumption using a large dataset of residential buildings. This study also examines the effect of the building clusters on the model performance. The novelty of this paper is to develop a model that enables designers input key features of a building design and forecast the annual average energy consumption at the early stages of development. This result reveals DNN as the most efficient predictive model for energy use at the early design phase and this presents a motivation for building designers to utilize it before construction to make informed decision, manage and optimize design.

Purpose: This paper examines the categories of property attributes that are important to UK university students in their online accommodation search. It also analyses the volume of information displayed regarding the property attributes and explores the influence of some of the information provided on the attractiveness and by extension, the booking potential of the property. Design/methodology/approach: The authors use data from an online student accommodation listing platform – student.com – which contain tangible and non-tangible property attributes, and the data are analysed using a hedonic regression model. Findings: The results show that purpose-built student accommodation's (PBSA) tangible and intangible attributes are important to students in their online accommodation search, although, these attributes vary in impact. The study also reveals that failure to display key information of a PBSA may reduce the attractiveness of the property. Originality/value: The empirical evidence on student accommodation ex ante preferences and choices is limited, particularly as it relates to online accommodation search in a UK context. The authors' approach to identify the application of the search theory to the student accommodation search process is particularly unique.

Purpose This paper aims to establish the most underlying factors causing construction projects delay from the most applicable. Design/methodology/approach The paper conducted survey of experts using systematic review of vast body of literature which revealed 23 common factors affecting construction delay. Consequently, this study carried out reliability analysis, ranking using the significance index measurement of delay parameters (SIDP), correlation analysis and factor analysis. From the result of factor analysis, this study grouped a specific underlying factor into three of the six applicable factors that correlated strongly with construction project delay. Findings The paper finds all factors from the reliability test to be consistent. It suggests project quality control, project schedule/program of work, contractors’ financial difficulties, political influence, site conditions and price fluctuation to be the six most applicable factors for construction project delay, which are in the top 25% according to the SIDP score and at the same time are strongly associated with construction project delay. Research limitations/implications This paper is recommending that prospective research should use a qualitative and inductive approach to investigate whether any new, not previously identified, underlying factors that impact construction projects delay can be discovered as it followed an inductive research approach. Practical implications The paper includes implications for the policymakers in the construction industry in Nigeria to focus on measuring the key suppliers’ delivery performance as late delivery of materials by supplier can result in rescheduling of work activities and extra time or waiting time for construction workers as well as for the management team at site. Also, construction stakeholders in Nigeria are encouraged to leverage the amount of data produced from backlog of project schedules, as-built drawings and models, computer-aided designs (CAD), costs, invoices and employee details, among many others through the aid of state-of-the-art data driven technologies such as artificial intelligence or machine learning to make key business decisions that will help drive further profitability. Furthermore, this study suggests that these stakeholders use climatological data that can be obtained from weather observations to minimize impact of bad weather during construction. Originality/value This paper establishes the three underlying factors (late delivery of materials by supplier, poor decision-making and Inclement or bad weather) causing construction projects delay from the most applicable.

This study investigates numerous competencies expected from civil engineering graduates based on employers’ perspectives in Indonesia to provide an understanding of employers’ requirements related to knowledge, skills and personal traits. Using the employers of civil engineering graduates as the study population, both qualitative and quantitative approaches were adopted for data collection and analysis, following an exploratory sequential mixed method design. The results, developed from a factor analysis, show that the underlying skills, knowledge and general traits that can give competitive advantages for graduates include 10 elements. The most significant components consist of interpersonal management skills, personality traits and entrepreneurial and business skills. As the first two factors, categorised as soft skills associated with traits and character, were the most important, the implication is that employers tend to prioritise building strong characters, even when recruiting or selecting employees with limited technical skills normally obtained during university studies. Other competencies expected of Indonesian civil engineering graduates discussed in the article include digital skills, as well as core knowledge of civil engineering, such as design and geotechnics skills. The findings will benefit universities seeking to enhance their graduates’ employability as well as students who are hoping to be ahead of the game. The article also provides insights for civil engineering employers who may want to understand the key skills and competencies in demand by other employers of civil engineering graduates in Indonesia.

Purpose: To foster effective implementation of Building Information Modelling (BIM), it is guided by standards and protocols that require files naming in a format, containing a string of letters and digits in a tightly defined manner, which is perceived to be time-consuming, error-prone and serves as a barrier to BIM adoption. In this paper, a BIM-based plug-in solution (Auto-BIMName) that facilitates automated naming in compliance with BIM standard is presented. Method: The Auto-BIMName portal has an Information Management System (IMS) for generating Master Information Delivery Plan (MIDP), which serves as pre-requisites to effective file naming. Once the naming schema is implemented through text input controls for a project name, volume, level and number, the Revit plugin communicates with its IMS to fetch the name string or concatenate the string in line with the ISO 19650 convention, where the IMS is unused. The system was validated through a simulated collaborative project. Findings: System testing and evaluation confirmed that the Auto-BIMName will ease the process of file naming, thereby facilitating collaboration efficiency, naming consistency across project teams and lifecycle stages, ease of file naming, time-saving, and inducement for BIM implementation, among others. By linking information from MIDP in BIM execution plan (BEP), the platform enhanced information management processes and improved coordination across project teams and lifecycle stages. Originality/Value: Apart from demonstrating how the autonomated naming platform enhances project performance, information management and coordination, the paper provides a practical demonstration of how the construction industry will benefit from enhanced digitalisation and process automation.

Abstract

The struggles to practise sustainable construction are not gaining the desired traction in Nigeria. This study established the likely barriers to successful application of sustainable construction in the Nigeria construction industry and factors to overcome the possible barriers. A quantitative approach was used for the study and a questionnaire survey was conducted among the professionals and other stakeholders. A descriptive method was used in analysing the collected data. Among the highly ranked sustainability barriers to construction practice are poor sustainability education in academic institutions, lack of incentives for designers to facilitate sustainable design, ignorance of lifecycle cost benefits, sustainable construction regarded as low priority and other issues take priority, and resistance to cultural change in the industry. The research recommends adequate sustainability education in academic institutions to positively impact the required cultural change in the industry. There is call for proper government policies that support implementation of sustainable construction practices. The study also advances the need for construction professionals and stakeholders to embrace the concept of sustainability education through continuing professional development and or postgraduate studies to improve the thinking and practicability of sustainable practice of construction in Nigeria.

Purpose: Effective procurement of infrastructure is linked to the attainment of the sustainable development goals set by the United Nations. While the capacity of organisations is generally thought to be related to organisational performance, there is a lack of empirical insights concerning the contribution of procurement capacity of public organisations towards the attainment of procurement objectives in infrastructure procurement. Thus, it is unclear which aspects of the capacity of public procurement organisations contribute the most to the attainment of procurement objectives in the procurement of infrastructure. This research sought to address this gap. Design/methodology/approach: The research used a survey of public procurement professionals which yielded 590 responses. Findings: Exploratory factor analysis of 23 organisational capacity items revealed three components of organisational procurement capacity: ‘management of the procurement process’; ‘human and physical resources’; and ‘financial resources and management’. Multiple regression modelling of the relationship between the components and the attainment of 12 procurement objectives further reveals that there is a significant positive relationship between the three components and all the objectives. However, ‘management of the procurement process’ emerged as the greatest contributor to the attainment of seven objectives, whereas ‘human and physical resources’, and ‘financial resources and management’ were the greatest contributor to the attainment of one objective and four objectives, respectively. Originality/value: The study provides strong empirical justification for investment in the development of procurement capacity of public agencies involved in procurement of infrastructure. Furthermore, procurement capacity development of specific capacity components can be prioritised based on the relative contribution of capacity components to the attainment of desired procurement objectives. This should be useful to government policy makers as well as multilateral organisations that fund infrastructure and procurement reforms in various countries.

Despite the booming construction activities in Indonesia, and increasing adoption of BIM across the globe, there has been a little mention of BIM as well as its poor implementationin Indonesia. This study investigates the level of BIM awareness, knowledge, perceived benefits and barrier to its implementation among local construction businesses in Indonesia. The research was conducted through structured questionnaires. The respondents were requested to rate the significance of some identifiedfactorsaccording to a five-point Likert scale. Data collected from the survey was analysed using SPSS and evaluated using reliability analysis and descriptive statistics to present the characteristics of the study variables. The study shows that while most respondents recognized Revit and ArchiCAD as BIM enabling tools, there is a need for more sensitisation of local contractors on the purpose and benefits of BIM adoption. The high cost of software and hardware, lack of knowledge, and lack of awareness of BIM were the main barriers to its implementationin AEC companies. The study was carried out within Palembang region and was limited to local construction professionals, with the exclusion of expatriates. Since BIM has facilitatedperformance and productivity in many nations, it is expected that the technology would be valuable to the Indonesian construction industry. Local Indonesian businesses would need to be motivated to adopt BIM technologyfor their advancement.

Using 693 000 datacells from 33 000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine, multiple discriminant analysis (MDA), and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to have only a slightly better accuracy than LR and MDA. The ANN's number of units in the hidden layer and weight decay hyperparameters were consequently tuned using the grid search. Tuning process led to tedious machine computation that was aborted after many hours without completion. The state of art big data analytics (BDA) technology was, for the first time in failure prediction, consequently employed and the tuning was completed in some seconds. Mean accuracy from cross validation was used for selection of the model with best parameter values, which were used to develop a new ANN model that outperformed all previously developed models on test data. Subsequent use of selected variables to develop new models led to reduced tuning computational cost, but not improved performance. Since the real-life effect of a misclassification cost is greater than the tedious computation cost, it was concluded that BDA is the best compromise

Accurate prediction of potential delays in public private partnerships (PPP) projects could provide valuable information relevant for planning and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behavior within large samples of projects, which are increasingly relevant for accurate prediction

Despite the increasing significance of embodied impacts of buildings, efforts to reduce their environmental footprints have been concentrated on the operational impacts of buildings. This study investigates the changing significance of embodied carbon over the entire life cycle of whole buildings. A case study of an office building was modelled with Revit, and sensitivity analyses of the modelled building were performed by varying the material specification and energy use pattern for seven other typologies. Using Revit, BIMWASTE tool, ATHENA Impact Estimator and Green Building Studio, comparative life-cycle analyses were carried out for the eight building typologies. The study suggests that notwithstanding the enormous impacts of the operational stage on life-cycle carbon of fossil fuel-based buildings, embodied impacts could vary between 8.4 and 22.3%. A key determinant of the proportional impacts of embodied energy is the nature of materials used for building construction. Similarly, embodied impacts of buildings become more significant and could contribute up to 60% of their life cycle impacts as they become more energy-efficient during their operational stage. As the study confirms the varying significance of embodied energy as construction materials and energy use patterns change, it implies the need for policy measures based on a whole life assessment methodology, instead of the usual ways of giving sole importance to the operational impacts of buildings. With buildings becoming more energy-efficient during their operational stage, there is an urgent need for an increased focus on the embodied impacts of buildings, especially as renewable energy resources are becoming widely adopted.

A significant proportion of construction projects are failing to achieve their deadline finish dates. This advocate for solutions that could address the root causes of time impacting risks, leading to the use of 4D BIM for project planning. This study investigates the impacts of 4D BIM on construction projects. An exploratory sequential mixed method research was conducted to initially explore the topic via interviews and literature review, and, subsequently, the themes derived were put into questionnaires to elicit expert knowledge on a wider industry scale. The data were analysed using thematic analysis, reliability analysis, Kruskal-Wallis test and factor analysis. Across the objectives around the impacts of 4D BIM on project reliability, monitoring and diagnosis, the findings presented eight key ways the 4D BIM support project performance. Examples of component factors that were raised was planning efficiency to enhance planner output, assessment and directive with a better comparison of planned and actual progress, and thorough/comprehensive risk reflection to cover wide ranges of issues. Upon further reflection, the finding highlighted the issues of the lack of shared responsibility outside of the planner and BIM coordinator, severe lack of understanding and training regarding 4D BIM and complexity of carrying out the process effectively.

The need to use Building Information Modelling (BIM) for Construction and Demolition Waste (CDW) minimisation is well documented but most of the existing CDW management tools still lack BIM functionality. This study therefore assesses the expectations of stakeholders on how BIM could be employed for CDW management. After a review of extant literature to assess the limitations of existing CDW management tools, qualitative Focus Group Interviews (FGIs) were conducted with professionals who are familiar with the use of BIM to understand their expectations on the use of BIM for CDW management. The 22 factors identified from the qualitative data analyses were then developed into a questionnaire survey. The exploratory factor analysis of the responses reveals five major groups of BIM expectations for CDW management, which are: (i) BIM-based collaboration for waste management, (ii) waste-driven design process and solutions, (iii) waste analysis throughout building lifecycle, (iv) innovative technologies for waste intelligence and analytics, and (v) improved documentation for waste management. Considering these groups of factors is key to meeting the needs of the stakeholders regarding the use of BIM for CDW management. These groups of factors are important considerations for the implementation and acceptance of BIM-based tools and practices for CDW management within the construction industry.

The bankruptcy prediction research domain continues to evolve with many new different predictive models developed using various tools. Yet many of the tools are used with the wrong data conditions or for the wrong situation. Using the Web of Science, Business Source Complete and Engineering Village databases, a systematic review of 49 journal articles published between 2010 and 2015 was carried out. This review shows how eight popular and promising tools perform based on 13 key criteria within the bankruptcy prediction models research area. These tools include two statistical tools: multiple discriminant analysis and Logistic regression; and six artificial intelligence tools: artificial neural network, support vector machines, rough sets, case based reasoning, decision tree and genetic algorithm. The 13 criteria identified include accuracy, result transparency, fully deterministic output, data size capability, data dispersion, variable selection method required, variable types applicable, and more. Overall, it was found that no single tool is predominantly better than other tools in relation to the 13 identified criteria. A tabular and a diagrammatic framework are provided as guidelines for the selection of tools that best fit different situations. It is concluded that an overall better performance model can only be found by informed integration of tools to form a hybrid model. This paper contributes towards a thorough understanding of the features of the tools used to develop bankruptcy prediction models and their related shortcomings.

This study investigates project financiers’ perspectives on the bankability of completion risk in Private Finance Initiatives and Public Private Partnerships (PFI/PPP) mega projects. Using a mixed methodology approach, focus group discussions with financier stakeholders in UK’s PFI/PPP industry were used to identify 23 criteria relevant for evaluating completion risk in funding applications. These criteria were put in a questionnaire survey to wider audiences of financiers of PFI/PPP projects in the UK. Series of statistical tests were performed, including Reliability Analysis, Kruskal-Wallis Non-Parametric Test, Descriptive Statistics, Principal Rank Agreement Factor (PRAF) and Regressions Analysis. After identifying 21 reliable criteria influencing the bankability of completion risk, the general agreement of three major financier stakeholders (Senior Lenders, Equity Financiers and Infrastructure Financiers) on all the criteria were examined through Kruskal-Wallis test and PRAF. A regression model, constructed and validated with input from another team of expert financiers, revealed five key criteria influencing the bankability of completion risk in PPP mega projects. These include (1) Construction contractor with years of experience of successful completion of mega projects, (2) Construction Contractor’s financial strength, (3) Existence of Tried-and Test Technology for the construction of project, (4) Availability of Independent Technical Consultant (ITC) and (5) Existence of Fixed Price Turn Key (FPTK) construction contract. The research findings will provide PFI/PPP contractors and clients with valuable strategies for satisfying financiers’ requirements in delivering large-scale Infrastructure PPP projects

Although the UK Guarantee Scheme for Infrastructures (UKGSI) was introduced in 2012 to address the huge financing gap for critical infrastructures, PFI sponsors have so far guaranteed only few projects. Many stakeholders in the project finance industry have blamed this situation on lack of general understanding of strategies for harnessing the benefits of government guarantees. The study therefore investigates the perspectives of PFI/PPP stakeholders in the UK on critical factors influencing approval for government guarantee using the UK guarantee scheme for Infrastructure as a focal point. Using a mixed methodology approach, the study identified 26 important criteria used in evaluating government guarantee applications through focus group discussions with PFI stakeholders. These criteria were put in a questionnaire survey to 195 respondents. Through factor analysis, five (5) critical factors determining successful government guarantee application were unravelled. These include (1) compliance with UK National Infrastructure Plan, (2) demonstration of project bankability and risk management, (3) value for money (4) proof of dependence on the guarantee and (5) certainty of planning commission's approval. Results will facilitate an in-depth understanding of critical factors for accessing government guarantees, while also improving the bankability of prospective PFI projects.

This study identified and evaluates the causes of building collapse, and the preventive measures that can avert such occurrences with the high frequency in the number of collapsed structures scattered around the length and breadth of Nigeria. The method employed in data collection includes the administration of a questionnaire to professionals in the Nigerian construction industry. The use of descriptive statistical techniques such as frequency distribution, percentages and mean response analysis were used to analyse data. The study identified the major causes of building collapse as weak/faulty foundations, inefficient stringent quality control in material utilisation and management, boycotting the professionals, absence of proper site investigation, and the engagement of inexperienced personnel. Some preventive measures were made as part of lasting solutions to tackle the challenges. These includes the supervision of construction works by professionals, education enlightenment on the public on the need to prevent building collapse rather than managing situations, employment of competent professionals, issue building approvals before construction commences and involvement of structural engineer in a project that goes beyond one floor. In general, this study has been able to provide a comprehensive view on the causes and the preventive measures of building collapse

The substantial amount of energy consumption in buildings and the associated adverse effects prompts the importance of understanding building energy efficiency. Developing an energy prediction model with high accuracy is considered one of the most effective approach to understanding building energy efficiency. Therefore, various studies have developed diverse models for predicting building energy consumption focused on the current building stock. However, to ensure future buildings are constructed to be more energy efficient, it is essential to consider energy efficiency at the design stage. Machine Learning (ML) algorithms are considered the most contemporary and best method for prediction, and these algorithms (such as Support Vector Machine (SVM) and Decision Tree (DT), among others) have gained much attention in the field of energy prediction. However, no study has explored the application of hyper parameter tuning and feature selection methods in developing a design stage Machine Learning (ML) energy predictive model. In this research, nine machine learning classification-based algorithms were compared for energy performance assessment at the design stage of residential buildings. Additionally, feature selection and hyper parameter tunning were implemented. The result shows that it is possible to develop a high performing ML model for building energy use prediction at the design stage. Furthermore, Gradient Boosting (GB) outperformed the other models with an accuracy of 0.67 for predicting building energy performance.

International Sustainable Ecological Engineering Design for Society (SEEDS) Conference Proceedings2024Conference theme: Achieving Resilience through Sustainable Digitalization and Ecological Engineering in the 21st Century.In the year 2023, we find ourselves in a world transformed by rapid advancements in digital technology. Generative Artificial Intelligence (AI), exemplified by models like ChatGPT, has emerged as a transformative force that is reshaping industries and revolutionizing the way we approach problem-solving, creativity, and sustainability. Generative AI, with its ability to generate human-like text and assist in complex decision-making processes, has found applications far beyond its origins. In 2023, industries spanning from healthcare to finance, from education to entertainment, have witnessed significant shifts in the way they operate. AI-driven insights, automation, and creative assistance have become the new norm, empowering professionals across sectors. In the field of ecological engineering, generative AI plays a pivotal role in the design and optimisation of sustainable solutions. It enables engineers and researchers to simulate and model intricate ecological systems with unprecedented accuracy, leading to more efficient resource management and environmentally friendly designs.At the nexus of digitisation and sustainability, we find an evolving landscape where technological advancements not only complement but also enhance the core tenets of Sustainable Ecological Engineering Design for Society (SEEDS). While we embrace the vast potentials of digital technologies in improving efficiency, predictive capabilities, and data-driven decision-making, it is crucial to integrate these tools without losing sight of the fundamental principles of ecological design. These principles, centered around harmony with nature, resource conservation, and sustainable development, remain as relevant today as they ever were. The challenge and opportunity for SEEDS 2024 lie in marrying the innovation and agility of these advancements with the wisdom and foresight of ecological design. This integration aims to foster solutions that are not only technologically advanced but also deeply rooted in ecological responsibility and sustainability.Thus, this year’s conference serves as a bridge between three essential elements:• Embracing cutting-edge innovations• Preserving time-tested ecological wisdom• Fostering resilience in our communities and ecosystemsThis year we ask:How do we leverage the advancements in digital technology and industry 4.0 to promote sustainable and ecological engineering for society?

International Sustainable Ecological Engineering Design for Society (SEEDS) Conference Proceedings2022Conference Theme: United Nation’s Sustainable Development GoalsIt has been thirty-five years since the seminal report Our Common Future (often referred to as the Brundtland Report), published by the World Commission on Environment and Development, proposed progressive environmental strategies and recommendations towards the attainment of the United Nations “A Global Agenda for Change”. Since then, we have seen many pivotal international actions, summits, frameworks, protocols, conventions, and declarations proposed. Amongst the most important was the Millennium Development Goals (MDGs), established by the United Nations in 2000, with 191 nations committing towards their delivery by 2015. Moving forward, the United Nations “2030 Agenda for Sustainable Development”, adopted by all Member States in 2015, proposed a shared blueprint for peace and prosperity for people and the planet, both now and into the future. At its core are the Sustainable Development Goals (SDGs), which are an urgent call for action by all countries to join in a global partnership towards their achievement. We are almost mid-way through the timeframe for the delivery of the seventeen SDGs so it is apt that society takes an opportunity to reflect on many of the triumphs towards their realisation and also seek steer on the directions of their future travel.Drawing on the themes of all the SDGs, the 2022 International Sustainable Ecological Engineering Design for Society (SEEDS) conference offers an integrated platform for academics and practitioners to reveal their personal and collective contributions towards the seventeen priorities.

International Sustainable Ecological Engineering Design for Society (SEEDS) Conference Proceedings2023Conference theme: Decarbonise or Demise - implementing zero carbon now.This year we ask:What must we do now to enable transformation towards carbon conscious communities; enabling a resilient carbon neutral future?The International SEEDS Conference aims to bring together experts from around the world to focus on changes required to minimise the impact of human activity on the environment. This year, the conference invites papers aligned to the three main themes of the Suffolk Sustainability Institute. The themes are interdependent and resonate with the imperatives we face in addressing the climate crisis, whether through technological transformation, community collaboration or resource management – each theme addresses issues that we know are vital when changing and challenging practice within our built environment. The arguments are well rehearsed, and this year we have witnessed another COP meeting. SEEDS provides an opportunity for us to share knowledge and experience to implement the research and evidence-based change needed within society to respond to and address ecological and climate impacts.Three main conference themes:Blue/Green Infrastructure (Technology – Smart Solutions for Future Smart Cities and Buildings, Data Science and IOT; Ecology – Nature Based Solutions, Ecosystems Services, Natural Systems, Biodiversity Net Gain)Sustainable Healthy Communities (Community Engagement; Smart Healthy Living/Health and Wellbeing; Behavioural Science to embed Change)Energy and Resource Management (Energy Efficiency and Sustainable Production; Materials Engineering within the Built Environment; Resource Efficient solution and a Circular Economy)

The International Conference for Sustainable Ecological Engineering Design for Society (SEEDS) brings together experts from around the world to focus on changes required to minimize the impact of human activity on the environment. The research presented at the conference directly addresses the imperatives faced in addressing the climate crisis, whether through technological transformation, community collaboration, or resource management – all vital issues when changing and challenging practices within our built environment. The SEEDS Conference is designed to share the knowledge and experience needed to implement research and evidence-based societal change to respond to and address ecological and climate impacts.

To operate successfully, an asset-centric organisation must recognise the need of visually validating the placement of its constructed assets. This is important to organisations but there are limitations and challenges. Building Information Modelling (BIM) has been widely adapted, and its establish methods and technologies offers some potentials in benefiting the highways sector. Hence this study involved the development of a BIM-based protocol and decision framework for real-time collection, validation, and handover of attribute data for National Highways' major schemes, using machine learning from images collected from drone flights. The study has integrated qualitative and quantitative approaches at rigorous and extensive stages, following the principles of critical realism theory and exploratory sequential mixed methods. The qualitative research further informed the design of a questionnaire which was used to elucidate broader industry experts’ perspective that ultimately guided the design, development and validation of machine learning-enabled real time asset information modelling. A new protocol to overcome these limitations by applying Machine Learning algorithms for Mask RCNN (Region based Convolutional Neural Networks) and recognising the assets of roads with geospatial images obtained from drones. The prototype looked at a linear asset, as they are the most difficult to capture. Using 150 images of the chosen asset type, these were labelled then processed using machine learning which then highlighted the assets it had learnt, allowing the output to be sent to client databases in there required file format. As proposed in the framework and validated through a case study the prototype effectively showcased how drone photogrammetry, powered by Machine Learning, a subset of Artificial Intelligence and Building Information Modelling (BIM) processes, can capture assets in real-time. This process reduces the time for redlining and negates the need for on-site surveys, adding value and reducing programme time.

The construction industry has been noted to be impervious to change. Over time, contractors have been found to be slow in implementing strategies to reduce carbon during construction projects. Hence, it is imperative to unearth factors that could be contributing to such sluggishness as well as those capable of motivating and promoting the carbon reduction behaviour of contractors. This study, therefore, aims to achieve this. To attain this, a comprehensive literature review and survey amongst construction professionals with carbon reduction experience in the UK was conducted. The data collected were analysed by utilising numerous quantitative analysis techniques. The research findings suggest several drivers, barriers and KPIs could influence contractors’ behaviour in reducing construction carbon. The drivers include carbon literacy, motivation and incentives, procurement and integration, and cost and profitability. The barriers are resources and prioritisation, policy and standards, risk and commitment, and awareness and complexity. The carbon KPIs resulting from the study include those related to transportation and construction-installation stages of the construction process. The transportation stage KPIs are personnel and location, resource management, and percentage of renewables and the construction-installation stage KPIs include waste, services, operations, product, construction method and machinery, renewable energy, and material. In addition, the contractors’ carbon reduction behaviours found in this study are consummate and pragmatic behaviours. Furthermore, this research produced three structural pathway models illustrating the influence of the drivers, barriers and KPIs on the carbon reduction behaviours of contractors, thereby, extending the construction process decarbonisation body of knowledge. The models indicate that carbon literacy is the only driver with a significant positive influence on contractors’ pragmatic and consummate carbon reduction behaviour. Likewise, resources and prioritisation was found to be the only significant barrier impeding contractors’ consummate behaviour, and only construction-installation KPIs have a iii considerable influence on the consummate behaviour of contractors in reducing carbon. Consequently, a carbon management protocol was developed to guide the carbon management performance of contractors and steer the attainment of construction decarbonisation. The outcome of this research could provide direction to construction clients and policymakers in instituting effective strategies focused on the identified factors to support the decarbonisation of built environment projects.

Sustainable development can be attained at a microlevel and having smart campuses around the world presents an opportunity to achieve city-wide smartness. In the process of attaining smartness on campuses, the elements requiring attention must be investigated. There are many publications on smart campuses, and this investigation used the bibliometric analysis method to identify such publications produced over the last decade. A matrix of 578 nodes and 3217 edges was developed from 285 publications on smart campus construction and procurement. Fifteen cluster themes were produced from the bibliometric analysis. The findings revealed that China contributed 48.4% of all published articles on the smart campus. The findings presented a framework from the cluster themes under the four broad infrastructure areas of building construction or repurposing, technology and IT network, continuous improvement, and smart learning and teaching management. The implications of the findings identified that IT project management, traditional procurement strategy, and standard forms of contracts such as the New Engineering Contract (NEC) and the Joint Contract Tribunal (JCT) are applicable in the procurement of smart cities.

This study explores the measures that construction businesses are putting in place to mitigate the risks of litigation associated with potential contract breaches due to COVID-19. Field study and survey research was used to accumulate data for the preliminary study phase, whose results were used in the construction of the research questionnaire to elicit responses for the qualitative study phase. Using descriptive statistics, exploratory factor analysis and reliability analysis, data from the quantitative research study phase was analysed to reveal the top and underlying strategies adopted by the construction firms in avoiding contractual disputes. These adopted strategies include maintaining good relationship with contractual partners, quick report of any potential for dispute, collaboration with contractual parties for goodwill, early decision on site closure and studying of contractual terms for notice period information. Following the discussion on the underlying measures, the top-rated adopted strategies were identified and discussed in the paper. This study provides an opportunity to learn from the measures adopted by construction firms in avoiding contractual disputes precipitated by the Covid-19 pandemic. This will aid the companies in minimizing risks, reduce spending and uncertainties in order to focus attention on maintaining existing contractual relationships to secure future businesses.

The contribution of smart campuses to smart cities’ development and vice versa has been elucidated in extant literature. The micro-transfer of smart technologies and probable procurement and contracting models remain critical for such contributions to take place. This study used a systems thinking approach to establish the interrelationships existing between smart campus technologies which can be scaled towards the development of smart cities and assess the critical failure factors negating the micro-transfer of these technologies to smart city development initiatives in developing countries. To achieve its objective, the study adopted a phenomenological research design wherein qualitative data was elicited from a purposively selected sample of seven interviewees in South African Universities. The next phase of the analysis involved a thematic approach for the production of a causal loop diagram (CLD) reflecting the interrelationships between the abovementioned facets and the associated impact on the transference of knowledge and technologies from smart campuses to smart cities. This CLD was subsequently validated by a cohort of five experts. Findings from the validation phase were incorporated in an improved CLD which provided different archetypes for engendering successful transference. This study holds salient implications for universities, cities and other stakeholders seeking to engage in a quadruple helix innovative arrangement for smart campus/city development. This study concluded that smart campuses could act as living labs for future smartness of cities globally. Government funding and willingness to produce smart cities from campuses is a fundamental feature of creating smart infrastructure in cities.

To address the multifaceted challenges facing the construction industry, Building Information Modelling (BIM) has been envisioned as a transformative approach. In line with this development, the paper presents a development and implementation of a BIM-based plugin, AutoBIM Carbon Calculator, which is integrated with the Revit software, with a functionality that supports the estimation and management of embodied carbon in construction projects. The paper outlines the AutoBIM design and development methodology, which is based on participatory quasi-experimental setup approach, involving automated approach for data extraction from BIM models, and carbon calculation algorithm that is integrated with carbon convention factor library. Based on a case study, evidence shows the ability of the AutoBIM calculator in precisely reducing time spent on carbon calculation while also automatically identifying carbon hotspot to facilitate design optioneering for carbon effectiveness. In addition, a case study project demonstrates 14% carbon reduction by supporting alternative design decision making. AutoBIM calculator, as a BIM-based plugin serves as a promising example of how custom-built solutions could address challenges facing the industry by highlighting the potential of BIM-based plugins to enhance productivity, efficiency, and sustainability.

Purpose The problem of long delay and waiting time in Disabled Facilities Grants (DFG) housing adaptation has been ongoing for years. This study aimed at constructing an innovative smart solution to streamline the housing adaptation process to prevent lengthy delays for disabled and elderly people. Design/methodology/approach The Adapt-ABLE approach is suggested based on a constructive research approach, where extensive theoretical development of the Adapt-ABLE concept is developed. It consists of four integrated platforms that undergo theoretical and analogical development and validations through applicable theories, a workshop, four brainstorming sessions and a focus group. Findings The proposed Adapt-ABLE approach utilises process optimisation techniques through an IT system for streamlining the process. The merits of the semi-automated system include the development of a preventive measure that allows measurement of suitability index of homes for the occupants, indicative assessment that shorten the application duration, procurement and contracting platform that utilises principles based on framework agreement and call-off contract, and a platform that standardised performance management for continuous improvement. Originality/value The Adapt-ABLE solution will cut the application journey of non-qualified applicants and suggest where help can be sought. The qualified applicants' application journey will also be shortened through an online indicative assessment regime and early online resources (means) testing. Overall, the proposed system reduces the waiting time, and timely delivery improves the applicant's quality of life by living independently. It will potentially save the NHS billions of pounds used to replace hips and residential care costs due to lengthy delays in the housing adaptations process.

Master Information Delivery Plan (MIDP) is a key requirement for BIM Execution Plan (BEP) that enlists all information deliverables in BIM-based project, containing information about what would be prepared, when, by who, as well as the procedures and protocols to be used. In a well-conceived BEP, the MIDP facilitates collaboration among stakeholders. However, current approaches to generating MIDP are manual, making it tedious, error-prone, and inconsistent, thereby limiting some expected benefits of BIM implementation. The purpose of the present research is to automate the MIDP and demonstrate a collaborative BIM system that overcomes the problems associated with the traditional approach. A BIM cloud-based system (named Auto-BIMApp) involving naming that automated MIDP generation is presented. A participatory action research methodology involving academia and industry stakeholders is followed to design and validate the Auto-BIMApp. A mixed-method experiment is conducted to compare the proposed automated generation of MIDP using Auto-BIMApp with the traditional practice of using spreadsheets. The quantitative results show over 500% increased work efficiency, with improved and error-free collaboration among team members through Auto-BIMApp. Moreover, the responses from the participants using Auto-BIMApp during the experiment shows positive feedback in term of ease of use and automated functionalities of the Auto-BIMApp. The replacement of traditional practices to a complete automated collaborative system for the generation of MIDP, with substantial productivity improvement, brings novelty to the present research. The Auto-BIMApp involve multidimensional information, multiple platforms, multiple types, and levels of users, and generates three different representations of MIDP.

Purpose: Delay in housing adaptation is a major problem, especially in assessing if homes are suitable for the occupants and in determining if the occupants are qualified for the Disabled Facilities Grant (DFG). This paper describes the development of two self-administered intelligent integrated assessment tools from the DFG Adapt-ABLE system: (1) The Home Suitability Assessment Platform, which is a preventive mechanism that allows assessment of the suitability of homes based on occupants’ mobility status and (2) an indicative assessment platform that determines if the applicants are qualified for the DFG to prevent lengthy delays. Design/methodology/approach: The adopted method aligned with a development study approach: a grounded literature review, a severity measurement approach, two stakeholder engagement workshops, four brainstorming sessions and four focus group exercises. The system development relied on Entity–Relationship Diagram (ERD) technique for data structures and database systems design. It uses DFG context sensitivity with alignment with DFG guidance, interlinkages and interoperability between the assessment tools and other platforms of the integrated Adapt-ABLE system. Findings: The assessment tools are client-level outcomes related to accessibility, usability and activity based on the assessment process. The home suitability platform shows the percentage of the suitability of a home with assessment results that suggest appropriate action plans based on individual mobility status. The indicative assessment combines the function of referral, allocation, assessment and test of resources into an integrated platform. This enables timely assessment, decision-making and case-escalation by Occupational Therapists based on needs criteria and the eligibility threshold. Originality/value: These assessment tools are useful for understanding occupants’ perception of their physical housing environment in terms of accessibility, suitability and usability based on basic activities of daily living and their mobility status. The indicative self-assessment tool will substantially cut down the application journey. The developed tools have been recommended for use in the CSJ Disability Commission report and the UK government Guidance on DFGs for local authorities in England.

This editorial introduces a special issue of the International Journal of Building Pathology and Adaptation (IJBPA). The special issue focuses on housing adaptations for healthy ageing by looking at the conceptual, theoretical, and practical approaches. Housing adaptation is an intervention to modify permanent physical home features to reduce environmental barriers and restore independent living. Adaptations promote independence, make it easier and safer for disabled and older people to live in their homes, and bridge inequalities, leading to a fairer society. It also restores confidence, dignity, self-respect, and their rights to accessible and adaptable housing. As the aged population is currently at its highest level in human history, healthy ageing and independent living are crucial for the elderly and people with disabilities. Ageing in place, a global concern, presents diverse challenges across different countries, each met with unique solutions.

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles’ textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations.

Construction procurement is increasingly becoming important because of the inherent risks in construction project. This chapter reviews risks and uncertainty in construction contracts and presents a framework for risk planning, assessment and management based on CMAA conditions of contracts. The analysis focuses on risks inherent in project management, cost management, time management, quality management, safety management and contract administration. The analysis of each of the management area of risk management covers three key areas; (i) management planning, assessment, (ii) risk assessment that is divided into two, risk identification and analysis (qualitative and quantitative analyses) and (iii) risk management in terms of response planning, monitoring and control. Agile specialist approach is used to demonstrate the application of the framework. The analysis shows that the construction management contracts services and practices and the contract documents explicitly identify, quantify, and develop responses and control project risks. In a broader sense, safety management can be said to be risk response development by setting the enhancement steps for opportunities and responses to threats.

The recent surge in light emitting diode (LED) lighting retrofitted into schools in the UK is as a result of the UK Government’s 2050 zero carbon pledge. However, the benefits and consequences of LED retrofit projects for staff and enablers and stakeholder knowledge gaps about LED lighting retrofitting have not been fully explored. The aim of this research is to determine the amount of savings in cost, carbon reduction and kilowatt usage and to confirm if repayment from energy and cost savings derived from LED retrofit school projects funded through the SALIX funding option in the UK would be enough to service the loan. Thus, it examines monetary and non-monetary benefits, internal project stakeholder knowledge gaps and the consequences of LED retrofit for the staff and enablers of a large community college in the UK which is funded through the SALIX funding option. Design/methodology/approach: The methodology relied on a hybrid research approach of a case secondary school through the review of literature, analysis of secondary data, focus group and questionnaire survey. The focus group consists of six key project stakeholders. The secondary data was sourced from the Project IGP [Individual Grade Proposal] and the Positive Energy Report from Zenergi, and the closed online questionnaire survey was used to sample 150 teaching staff and school enablers. Findings: The findings show that stakeholders lack project knowledge, trust and expertise/project comprehension. This is in terms of baseline information, LED technology/management, payback modalities, management of risks and ethical issues around environmental impact. The forecasted SALIX savings were not achieved in real-time, partly because it does not take into consideration the increase in energy costs over the payback period. However, the LED retrofit creates efficiencies; drives down energy costs and energy usage; and drives carbon reduction, helping pupils’ learning, improving productivity and performance, and finally leading to a better lighting environment for the school community. Originality/value: The study will help schools in the UK that intend to access SALIX finance for LED retrofits to understand the challenges and mitigate the risks. It will also help the government to understand the importance of adjusting the payback modalities to the base price when the retrofit was carried out for real-time savings to be made. The research would be useful in ensuring the proactive involvement of all the identified stakeholders in understanding the challenges and what the function entails.

Purpose The lack of a proper register to store, match and display information on the adapted property has led to a waste of resources and prolonged delays in matching the disabled and elderly people with appropriate properties. This paper presents the development of a Housing Adaptations Register with user-matching functionalities for different mobility categories. The developed system accurately captures and documents adapted home information to facilitate the automated matching of disabled/aged applicants needing an adapted home with suitable property using banding, mobility and suitability index. Design/methodology/approach A theoretical review was conducted to identify parameters and develop adaptations register construct. A survey questionnaire approach to rate the 111 parameters in the register as either moderate, desirable or essential before system development and application. The system development relied on DSS modelling to support data-driven decision-making based on the decision table method to represent property information for implementing the decision process. The system is validated through a workshop, four brainstorming sessions and three focus group exercises. Findings Development of a choice-based system that enables the housing officers or the Housing Adaptations Register coordinators to know the level of adaptation to properties and match properties quickly with the applicants based on their mobility status. The merits of the automated system include the development of a register to capture in real-time adapted home information to facilitate the automated matching of disabled/aged applicants. A “choice-based” system that can map and suggest a property that can easily be adapted and upgraded from one mobility band to the other. Practical implications The development of a housing adaptation register helps social housing landlords to have a real-time register to match, map and upgrade properties for the most vulnerable people in our society. It saves time and money for the housing associations and the local authorities through stable tenancy for adapted homes. Potentially, it will promote the independence of aged and disabled people and can reduce their dependence on social and healthcare services. Originality/value This system provides the local authorities with objective and practical tools that may be used to assess, score, prioritise and select qualified people for appropriate accommodation based on their needs and mobility status. It will provide a record of properties adapted with their features and ensure that matching and eligibility decisions are consistent and uniform.

Rapid development of infrastructure in Indonesia, coupled with escalating environmental concerns, has necessitated construction waste minimisation. Notwithstanding that the design stage of construction project delivery processes offers a significant opportunity for waste minimisation, current practices in Indonesia often overlook this stage by rather focusing on waste management during the construction stage. This study investigates the design measures for promoting waste efficiency within the Indonesia construction sector, thereby addressing a gap in existing practices. Employing a quantitative approach that was underpinned by review of extant literature, 468 construction professionals across different roles within the Indonesian construction industry were surveyed using random sampling technique. Results from exploratory factor analysis suggests the significance of optimising design processes to mitigate errors and rework, fostering stakeholder integration to pre-empt constructability and the need to leverage innovations such as the use of prefabrication and secondary materials. Design strategies that support integration, spatial adaptability and standardisation were found to be requisite to mitigating construction waste. Findings of this study suggest the need for a systemic change through policy and incentives for sustainable construction practices, embedding waste prevention into the procurement processes and early-stage collaborations among project stakeholders. Although the findings of this study align with international best practices, they equally provide a pathway for Indonesia to achieve sustainable infrastructural development without the negative impacts associated with waste management. Through findings that are tailored to the Indonesian construction industry and contribution to the broader global discourse on construction, this study provides actionable insights for both practitioners and policymakers.

Purpose: The increasing awareness of stress as an issue in the construction industry, and the increased realisation of the more effective approach of preventing stress as opposed to treating mental health has placed a great responsibility on industries that rely heavily on the performance of their workforce. In line with this, this research aims to investigate the causes of stress as well as the best strategies for improving professional resilience and stress management proficiency in the construction industry. Design/Methodology: Using exploratory sequential mixed method research design, the top factors that cause stress, mitigate stress, and to build resilience are identified and discussed. Findings: The findings show thematic similarities in the causes of stress, by displaying a reoccurring theme relating to the quality and lack of information, as well as poor communication among the construction professionals. This implies that poor collaboration and “over-the-wall syndrome” of the construction industry does not only contribute to defective output and reworks, it also has significant impacts on the health and well-being of the professionals. Recommendations are made on how to mitigate stress in the industry, by addressing its root-causes and implementing the identified mitigation strategies. This includes a realistic approach to budgeting and project duration, as well as improved communication and collaboration. Value: Finding of this study could help in mitigating stress and mental health concerns that are currently plaguing the construction industry.

The study investigates the factors contributing to the menace of Alcohol and Substance Misuse (ASM) in the construction industry, and its mitigation. Sequential exploratory mixed method design, involving interview and questionnaire were used for collecting data that were subsequently analysed using thematic analysis, factor analysis and Kruskal-Wallis test. Findings suggest that the problem of ASM is largely caused and exacerbated by mental strain, site working conditions, male dominance and ineffective HR management. Screening and educational approaches were confirmed as the carrot and stick methods for mitigating the menace in the construction industry. The finding implies that by making construction companies more accountable for the wellbeing of their workers, a change could be brought in the industry. These changes could be incentivised by initiatives like PREVENT to mitigate the crisis currently endemic in construction. The study suggests some practical measures for tackling the menace of ASM that is bedevilling the construction

Purpose Although there has been a surge in the adoption of building information modelling (BIM) in the construction industry, the small and medium-sized enterprises (SMEs) are still struggling and perceive its adoption as risky. The SMEs in developing economies are especially on the disadvantaged side of the digital divide. Extant studies have focused on large firms and there are scanty studies on the influence of the external environments on BIM adoption in SMEs. Thus, this study espouses institutional theory (INT) to examine the influence of coercive, mimetic, and normative pressures on BIM awareness and adoption in SMEs. Design/methodology/approach A quantitative approach was employed, and data were collected from the Nigerian construction SMEs via an empirical questionnaire survey using a sequential stratified and convenient sampling method. Hypothesized relationships between the coercive, mimetic, and normative pressure and BIM in SMEs were empirically tested using the partial least squares structural equation modelling (PLS-SEM) technique and the model was validated with the “PLSpredict” procedure. Findings The results revealed that coercive and mimetic pressures significantly influence BIM adoption in SMEs while normative pressures have the strongest influence on BIM in SMEs. Also, BIM awareness is an important predictor of BIM adoption. The findings also shed light on the influence of firmographics on BIM awareness and adoption in Nigerian SMEs. Originality/value The study empirically validates the applicability of INT and highlights that BIM adoption is not only influenced by internal responses to the need for efficiency but also by external pressures. It implies a clear need for intentional isomorphic pressures in driving BIM adoption in SMEs. The study employs the INT to explain a phenomenon that has not been theoretically explored in the context of SMEs in developing economies. Lastly, the study provided valuable insights into driving BIM adoption, together with the effective practical implications for implementation and potential research areas for further studies.

The main objective of the study was to identify the effect of mobile learning activities using social learning applications (apps) based on the principles of constructivist learning on students' learning achievement and behavioural patterns in a History of Art Course. Accordingly, two mobile apps, Pinterest and Piazza, were adopted in the present study. A one-group pre-test and post-test experimental design was implemented to evaluate the effectiveness of the proposed approach. The participants were 57 female students studying in the second semester of the Computer Arts program of a public sector women university of Pakistan. The intervention spanned 6 weeks, and comprised four assignments given alternately on Pinterest and Piazza each week so that the students could use both tools at least twice. Student performances were measured using a pre-test and post-test. The results revealed that there was a significant effect of using the social learning apps on the students' achievement in the History of Art Course. Low achievers gained significantly more than high achievers. The results suggest that well-designed activities using social learning apps need to be incorporated into the teaching of courses for undergraduate students.

The recent decade has seen increased attention focused on understanding category formation–a cognition ability of preschool aged children. Children organize their knowledge about real-world objects by categorizing them under some common properties or functions. In this study, an approach that models structural and functional categorization knowledge for developing mobile learning systems with dynamic categorization exemplars is proposed. A mobile application was implemented based on the proposed model for pre-schoolers (aged 3-6 years). Moreover, the quasi-experimental pre-test and post-test method was used to evaluate the effectiveness of the proposed knowledge-based application in terms of categorization ability learning. The results show that the children who experienced dynamically created categorization exemplars from the modelled knowledge achieved increased scores compared to those who followed the traditional teaching using books and worksheets.

Purpose - This study is aimed at investigating the implementation of circular economy (CE) strategies in a developing economy context with the view of understanding the construction and demolition (C&D) waste management dynamics. Design/Methodology/Approach - Sequel to a systematic review of literature, data was collected via Delphi survey from 22 and 17 Nigerian construction experts in the first and second rounds, respectively. Consensus was established after the stability of responses in the second round. Data were analysed using mean score ranking, rank agreement factor, and inter-rater analysis. Findings – 19 distinct CE strategies for managing C&D were identified. Subsequent assessment revealed mild implementation of these strategies across different construction stages, with the least implementation at the end-of-life phase, despite producing the most waste. Practical implications – The study revealed very mild implementation of CE strategies within the Nigerian Construction Industry (NCI) and highlighted the diverse views among the experts regarding the effectiveness of the strategies for managing C&D waste. Results from the study will offer an insightful guide for stakeholders and policymakers to understand the suitability of CE strategies at different stages of C&D waste for efficient policy development and decision-making. Originality/Value – This research contributes to the CE literature by synthesising the existing body of knowledge on CE strategies to manage C&D waste sustainably across the construction value chain of developing economies. It also establishes a foundation for prioritising effective CE strategies in the NCI and disseminating knowledge about ineffective related to waste management.

The decarbonisation of built environment projects is becoming increasingly significant in realising net zero goals in many countries. Even though construction contractors responsible for bringing design to fruition have been touted to be slow in adopting strategies that can facilitate this ambition. Therefore, it is vital to understand the drivers that could motivate these stakeholders to decarbonise the carbon footprint within their control. While equally investigating the geographical spread and temporal distribution of studies that have contributed to this development. In achieving this, a systematic review of the literature approach was adopted and identified studies were examined through content analysis. The result of the analysis yielded thirteen drivers from 20 eligible studies. Also, studies on the drivers of carbon reduction began to appear in literature in 2008 and overall, the UK, USA and Australia tend to dominate this research area. These findings suggest that there seems to be sparse research conducted in this knowledge area and more studies are required across the globe if the world is to mitigate the effect of climate change and attain its net zero ambition. Lastly, the outcome of this study might be beneficial for construction stakeholders and policymakers in developing strategies to support research and practice to decarbonise the built environment.

This study explores the behavioural safety measures for addressing accidents on Qatar mega projects which are infamous for their high rates of accidents and fatality. Using extant literature and brainstorming as sources of preliminary information, questionnaire was used for collecting quantitative data, which was analysed using reliability analysis and exploratory factor analysis. The finding suggests that there is a low level of behavioural safety awareness among the construction workers due to their lack of adequate safety knowledge, with the rate of accident exacerbated by use of improper safety gears or improper use of such gears. Putting production ahead of safety, due to urgency and timeliness of the projects, engenders poor disposition to safety among site workers. To turn the tides on the high rates of accidents and fatality, more proactive approaches to safety management is essential for engendering behavioural safety among the operatives. Such measures include enhanced communication and feedback on safety practices, increased use of safety signs to raise awareness, education and regular observation and appraisal of the employees. A key requisite for achieving a positive behavioural safety outcome is an increased commitment of the management team and the site supervisors who are expected to implement a robust safety policy as well as the carrot and stick approach for engendering positive safety behaviour. With behavioural safety management recognised as an effective approach for mitigating health and safety concerns on construction sites, this study suggests measures for mitigating unsafe behaviours, thereby reducing accidents and fatalities on the Qatari Mega projects.

Purpose: The outbreak of Covid-19 pandemic has tested the resilience of the construction industry, putting the safety of workers and overall businesses at risk. This study explores the different strategies adopted by construction companies to protect the health and wellbeing of employees, security of the construction sites and projects, and keep the overall business operational amid Covid-19 pandemic. Design/Methodology: A preliminary study that involves field study and survey research was used to collect data for the study. The results from the preliminary analysis served as inputs for constructing the questionnaire, which was analyzed using descriptive statistics, exploratory factor analysis and reliability analysis. Findings: The results reveal that the key underlying measures put in place by construction businesses include restricted site access, support bubbling of office and site staff, enhanced hygiene and social distancing protocol, contract risk identification and mitigation, self-isolation measures and heightened construction site safety. Along with a further discussion of the underlying measures, the top-rated strategies that were adopted by construction firms are also discussed in the paper. Originality/Value: As many construction companies remained opened handling essential projects amid the pandemic, the study presents the effective and efficient strategies that were used in ploughing through the trying times. This study provides the opportunity for construction companies that escaped early impacts of Covid-19 due to site closure and policy makers to learn from the strategies adopted by construction companies that were operational amid the pandemic.

Purpose This paper aims to identify and analyse the factors affecting the selection of effective cost control techniques in the UK construction industry and assess their importance. The study examines these key areas; the factors that have significant impacts on cost overruns, the most effective cost control techniques and the factors for selecting cost control techniques for a project. Design/methodology/approach The study relies on a mixed-method research approach; a qualitative exploration of the most effective cost control techniques and the factors affecting the selection of cost control techniques, followed by a questionnaire survey and follow-up interviews. Relative importance index (RII) is used for ranking the factors. Findings The budgeting technique is ranked first with-0.821RII, followed by cost forecasting-0.800RII and cashflow monitoring-00.733RII, as the most effective cost control techniques. On factors that influenced the choice of the techniques used, cost information/cost-related factors are ranked first with-0.611RII, followed by the size of the company-0.509RII and the effectiveness of the technique-0.572RII. Research limitations/implications Although the scope of the study was limited to the UK construction industry, the results could be interpreted for critical learning in other developed/developing countries. Originality/value Identifying and ranking the factors affecting the selection of effective cost control techniques in the UK construction industry has been the focal point of this study. The study also proposes a simple but effective model which can be used for critical learning on mitigating cost overruns and the effective use of cost control techniques in the construction industry.

The use of offsite methods of construction has long been recognised as a suitable method for increasing the supply of housing and addressing productivity in the construction industry. Whilst the literature is rife with the many benefits of the modern methods of construction as well as barriers to its implementation, its adoption remains relatively low. This study contributes to the discussion by investigating the strategies for further harnessing the benefits of the increasingly important method of construction as well as the measures for mitigating its challenges. In line with the tenet of phenomenological research, which seeks to explore the phenomenon from the perspectives of the industry experts, 12 interviews were carried out with construction professionals. The data were analysed using thematic analysis, thereby unravelling the emerging themes that emanated from the interviews. After identifying such benefits of offsite construction as increased built quality, waste mitigation, and time efficiency, among others, the strategies for maximising these benefits were presented. These include enhanced training, use of digital tools, standardisation of building components and more efficient pre-planning activities, among others. Similarly, impediments to the use of offsite construction techniques such as its high initial cost, negative stigma, non-supportive project delivery models and clients' resistance could be addressed through some measures. These measures include the development of new supply chain management model, training and education, enabling legislation and vertical integration within companies. This study will help to identify the measures for enhancing the adoption and implementation of offsite technologies in the UK construction industry. Future research is recommended to assess the government's role in being the driving force behind implementing prefabricationspecific policies and incentives to encourage its future use.

Structural retrofit of existing buildings for reuse remained one of the key steps toward decarbonisation of the existing housing stocks in the UK. This implies that any structural retrofitting procedure should aim at sustainability by ensuring net reduction in energy use with minimum cost, environmental and social impact. However, several factors impede the attainment of sustainable structural retrofit programs. In this study, quantitative data collection and exploratory factor analysis were used to investigate the factors that impede achieving sustainability in structural retrofitting of existing buildings. The study conducted a review of pertinent literature to draw up a list of potential impediments to sustainable structural retrofit. The lists were used to form Likert scale questionnaire that was administered to 126 professionals within the built environment sector in the UK. The data collected were subjected to reliability analysis and exploratory factor analysis using the SPSS IBM Statistics v24. The analysis revealed that there are four groups of barriers that impede sustainability in structural retrofitting of existing buildings. These are (i) cultural barriers involving factors that are characterised by human behaviour and interest; (ii) economic barriers involving cost functions; (iii) technical knowledge barriers involving education & skills factors and (iv) regulatory barriers involving legislation and policies around retrofitting old buildings. The findings of this study contribute to the broader discussion of sustainability within the built environment by increasing awareness of the key barriers to overcome to promote sustainable structural retrofit of existing buildings

With the emergence of vast technological advancements in the construction industry to address construction issues, BIM is foreseen as a potential utilization environment. To expand BIM performance in addressing contemporary construction issues, there is currently an uprising trend of plugin development. This paper reports a systematic review of BIM-based plugins, by exploring their nature, areas of application, and their implication on productivity and efficiency. Using a systematic review approach, the study narrows down vast literature and critically analyses 34 developed plugins from 34 different articles. Findings suggest that custom-build plugins are excelling in vast areas utilizing key construction processes like automation, including Health and Safety and Lifecycle assessments, as they are proven to drive productivity and efficiency towards cost and time savings as well as error minimization. Moreover, based on the commonalities between the extracted plugin systems, a framework that could facilitate a better understanding of the programming dynamics in developing BIM-based plugins is presented. Value: This paper offers future research of the insights needed to better understand the directions of BIM-based plugin development and captures BIM evolvement towards more problem-solving potentials.

Cost overrun is a global issue plaguing transportation infrastructure projects, and the rail industry in the United Kingdom is not exempted. This industry plays a significant role in stimulating the country’s economic development and growth in the United Kingdom (UK). Understanding the factors contributing to cost overruns in rail projects is viable to sustain its contribution, especially in the COVID-19 epidemic era. This study analyzed and predicted the impact of cost overruns on rail projects in the UK in a COVID-19 pandemic era through a sparse network and system dynamics approach. It leverages a review of existing literature and perceptions of 80 construction professionals purposively to establish the factors affecting cost overruns affecting the UK rail construction sector. Findings reveal a web of causations that highlights labor productivity, delay in the supply of construction materials and equipment, and access to the site as fundamental predictors of cost overruns during a COVID-19 pandemic era. The resulting findings are beneficial to assist project managers with the needed awareness to strategically plan and mitigate the potential rise in cost overruns of rail projects in the UK during the pandemic.

Purpose Small- and medium-sized enterprises (SMEs) within the construction sector are highly vulnerable to disruptions caused by political and economic decisions or even pandemics. This study evaluated the current operations of selected SMEs providing engineering design and consultancy services against Toyota Production System (TPS) lean tool. The purpose is to juxtapose SME operations and processes with TPS to ascertain the level of their operations conformity to the established TPS lean thinking tool. Design/methodology/approach This study used a qualitative data collection and analysis approach to evaluate the current processes of participating SMEs against Liker's 14 management principles of TPS. The data collected were analysed using thematic analysis to identify patterns and themes that emerged from the qualitative data. Findings The analysis revealed that focus on short-term goals, immediate profit and duplication of effort resulting from insufficient collaboration is currently creating waste in participating SMEs' operations. Hence, the implementation of TPS was recommended as a lean tool and a framework based on TPS lean tool was developed for improving the operations of SMEs. Research limitations/implications The study is limited to SMEs operating only as consultants providing project planning design within the construction industry. Data collection is limited to qualitative even though observations would improve the outcome of the study. Originality/value The study advances contemporary issues in promoting lean implementation in construction sector and developed an improved framework based on the TPS to enhance the performance of SME construction businesses.

Purpose Supply chain disruptions have a significant impact on overall project delivery. This study aims to identify the supply chain disruptive factors and develop a framework to mitigate the disruptive effects on the supply chain. Covid-19 and Brexit disruption and their longevity effects in the short, medium and long term on the supply chain are relied upon to develop the framework. Design/methodology/approach The study adopted a mixed-method approach with a sequential explanatory design. The main disruptive factors were identified through a literature review, and key factors were selected through a focus group exercise. A questionnaire survey was carried out to sample opinions from the practitioners; 41 questionnaires were received and analysed using the relative importance index (RII) method for ranking the factors and percentage frequency distribution to determine the longevity effects. Five follow-up semi-structured interviews were conducted over the telephone and later transcribed. Findings The results of Covid-19 disruption indicate that material cost increase ranked first (RII: 0.863), logistics cost increase and supply chain interaction ranked second and third, respectively. They have long-term, medium-term and short-term longevity effects, respectively. The lowest-rated factors were communication (RII: 0.561), staff shortages (RII: 0.629) and impact on relationships (RII: 0.639). The three most ranked Brexit disruptive factors are supply chain interaction (RII: 0.775), material cost increase (RII: 0.766) and logistic and haulage delay (RII: 0.717). The first two factors have long-term effects, and the logistics and haulage delays have a medium-term impact. The mitigating solutions suggested in the framework are collaborative working, stronger resilience to external forces and better transparency and communication that will lead to good relationships among the supply chain members. Research limitations/implications The scope of the study was limited to the UK construction industry; however, the pandemic effect on supply chain can serve as critical learning curve in other developed and developing countries. Practical implications The study will help the government and construction firms to understand the focal areas of importance in solving the supply chain disruption problems based on the effects of Brexit and Covid-19. The research would be useful in ensuring the proactive involvement of the government and contracting firms in their preparedness for similar events in the future. The results could be interpreted for critical learning in other developed/developing countries. Originality/value Identifying and ranking the supply chain disruptive factors affecting the small‐ and medium‐sized enterprises (SMEs) in the UK construction industry has been the focal point of this study. The study also proposes a simple but effective framework comprising the highly ranked factors, their longevity effects and mitigating measures. This will help the SMEs manage future/similar external events affecting the supply chain.

Health and safety is an important issue in workplaces, and despite safety procedures becoming more strict, serious accidents are still happening within the UK construction sector. This demonstrates poor performance in the implementation of safety procedures on construction sites. One of the key challenges is the unwillingness of the site workforce, especially the subcontracted operatives, to adhere to safety provisions on construction sites. As such, this study investigates the strategies for enhancing safe behaviour among subcontracted operatives in the UK construction industry. The study used exploratory sequential mixed method research, involving interviews and questionnaires as means of data collection, and thematic analysis, reliability analysis and exploratory factor analysis as methods of data analysis. The study suggests that various carrot and stick measures are expected to be put in place as part of the strategies for enhancing safe behaviour among subcontracted operatives. These include adequate enforcement of safety practices by the management, operative engagement and motivation, commendation and rewards, site safety targets, leadership style and motivation. Application of the suggested measures could enhance safety on construction sites, as it provides practical measures and solutions for inculcating safety behaviours among the site operatives who are most likely to be the victims of site accidents.

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.

Heritage or Historic BIM (HBIM), a specialised application of Building Information Modelling (BIM) for the preservation and management of historic buildings, offers transformational opportunities for the heritage conservation sectors. However, this has not been fully explored, with HBIM applications mostly used as mere archival documentation for heritage architecture. As such, this study proposes to investigate the opportunities and challenges in adopting HBIM in preserving and managing heritage buildings. The study adopts a qualitative research strategy comprising literature review and expert interviews to explore the perspective of heritage conservation stakeholders on HBIM. The collected data were analysed using thematic analysis to identify the current state of HBIM adoption, its benefits, and its challenges. Findings reveal that while HBIM offers significant opportunities, such as improved archival documentation, visualisation, and maintenance planning, its adoption remains limited due to high costs, lack of expertise, and resistance to new technologies. This study acts as a reference point illuminating the need for increased awareness, training, and investment in HBIM to fully harness its potential, positioning it as a crucial tool for the sustainable management of heritage assets. This study originality is in its primary focus on HBIM, an application that has been under explored unlike BIM.

Heritage or Historic BIM (HBIM), a specialised application of Building Information Modelling (BIM) for the preservation and management of historic buildings, offers transformational opportunities for the heritage conservation sectors. However, this has not been fully explored, with HBIM applications mostly used as mere archival documentation for heritage architecture. As such, this study proposes to investigate the opportunities and challenges in adopting HBIM in preserving and managing heritage buildings. The study adopts a qualitative research strategy comprising literature review and expert interviews to explore the perspective of heritage conservation stakeholders on HBIM. The collected data were analysed using thematic analysis to identify the current state of HBIM adoption, its benefits, and its challenges. Findings reveal that while HBIM offers significant opportunities, such as improved archival documentation, visualisation, and maintenance planning, its adoption remains limited due to high costs, lack of expertise, and resistance to new technologies. This study acts as a reference point illuminating the need for increased awareness, training, and investment in HBIM to fully harness its potential, positioning it as a crucial tool for the sustainable management of heritage assets. This study originality is in its primary focus on HBIM, an application that has been under explored unlike BIM.

This feasibility study explores the development of the HomeAI-Enable system, an AI-driven decision support platform designed to provide personalised housing adaptations and assistive technology recommendations for older adults and individuals with disabilities. The study follows a constructive research approach including focus groups and brainstorming exercises in addition to development research techniques. Three different datasets comprising one, five, and over 500 diseases sourced from reputable databases such as the NHS and Mayo Clinic were used. The datasets include symptoms, diseases and associated housing adaptation and assistive technologies for the identified symptoms and diseases. The system integrates these datasets to offer personalised solutions based on symptoms, disease, body functions, and environmental factors, using advanced techniques like fuzzy matching, natural language processing, and advanced machine learning models. The feasibility study involves the assessment of multiple AI models to determine their effectiveness in providing tailored recommendations for assistive technology and housing adaptations. A proof of concept was developed, and early technical evaluation demonstrates the viability of the proposed architecture and recommendation workflow for housing adaptation decision support. This study evaluates the technical feasibility of the system at a foundational stage within a broader research programme, with clinical validation involving occupational therapists and end users planned for subsequent phases.

Previous studies have focused on explaining the BIM adoption or implementation, and little is known about the diffusion trend. Thus, this study evaluates the BIM diffusion process in SMEs with a predictive machine learning modelling approach using the Nigerian construction industry as a case study. Empirical questionnaire was employed for BIM diffusion data collection, and the collected data was cleaned and balanced with hybridized SMOTE +Tomek links. Ensemble machine learning algorithms were applied to the collected data to develop predictive models for the BIM diffusion process. The optimized and best-performing models were stacked via a new model (ensemble of ensembles) and deployed in an interactive Python-based Application - BIM Diffusion Prediction System (BIM-DPS) - using Streamlit framework. The most important features for BIM awareness in SMEs are observability, top management support, and normative pressure, while the top predictors of intention to adopt BIM in SMEs are compatibility, top management support, and behaviour. These sets of the same features have different impacts on awareness and adoption. Also, these features are from technology, organisation and environment contexts of the TOE framework and underscore the social-technical nature of BIM, which should be reflected in strategies to drive proliferation. The study highlights the strong predictive performance of stacked ensemble models. It provides an easy-to-use application to forecast the behaviour of firms to mitigate risks and develop tailored interventions.

Artificial Intelligence is transforming the healthcare sector, providing innovative solutions to empower individuals and make medical support more personalized. This study introduces a novel AI-driven platform that links diseases and symptoms to relevant assistive technologies and housing adaptations, ultimately developing a tailored knowledge base for individuals diagnosed with complex chronic conditions such as muscular dystrophy. The platform development entails the integration of advanced Natural Language Processing (NLP) techniques and fuzzy matching algorithms into a user-friendly web-based interface. This enables successful interpretation of user input queries and generation of real-time tailored actionable insights and personalized recommendations for housing adaptation and assistive technologies. This research showcases a scalable, innovative method of patient care that revolutionizes the existing landscape by integrating new AI methodologies into healthcare databases to generate impactful and empathetic elderly and disabled care. The proposed system obtained a query resolution accuracy of 98% and aims to bridge critical gaps in healthcare and housing accessibility by offering solutions and a sense of empowerment to those navigating the challenges of chronic and progressive conditions.

In recent times, there has been an increasing attention on embodied carbon reduction of building construction projects. However, most of this attention have been concentrated on carbon emission related to materials used for building construction while construction activity related carbon emission seems to have been largely ignored. Hence, this paper examines this claim by analysing literatures in construction management journals published between year 2000 and 2021. The authors performed the analysis by examining the annual publication of research related to carbon reduction during the construction phase of building projects, geographical spread and/or institution of authors who have contributed to these studies, and key research themes covered. The systemic review of literature conducted shows that there seems to be very little research published relating to carbon reduction during building construction projects. Also, the findings suggest that carbon reduction during building construction project related research have only just been in mainstream publication in the last five years with this research largely domiciled in China, US, Australia, and Hong Kong. Furthermore, it was discovered that most of the existing research related to the focus of this paper was done in the context of life cycle analysis or assessment. Research gaps were highlighted, and future research path is proposed. It is likely that the findings of this study may arouse researchers with interest in construction carbon reduction and industry stakeholders alike.

Construction contractors have been touted to be a major player in reducing carbon emissions during the construction process due to their role in bringing building designs to life. Also, various carbon reduction strategies have been highlighted in assisting contractors in reducing the carbon emitted while carrying out construction activities. However, it is believed that contractors are quite reluctant to adopt these strategies for several reasons, such as a possible increase in cost and being contradictory to their existing construction procedures. This paper, therefore, aims to highlight the report of a preliminary study done through the adoption of a systematised review approach in synthesising literature to understand what the evidence is as regards how contractors contribute towards the reduction of carbon during building construction activities. The findings reveal that there is a paucity of research in this research area as only 12 papers were found relating to the focus of this study. The analysis of these papers revealed eight possible ways (e.g. adoption of lean management principles, adoption of eco-hauling concept, and utilisation of modern digital tools) in which contractors contribute to carbon reduction during building construction projects. The findings of this study will benefit contractors looking for a suite of measures to implement in reducing their building construction project carbon footprint while also serving as a piece of foundational knowledge in the research area of carbon reduction during building construction projects. Lastly, a future study area worth exploring is proposed.

Extant studies have highlighted numerous barriers to carbon reduction but none seem to have been done in the context of construction process in the UK. Hence, this study aims to bridge this gap by inves-tigating the barriers that are critical to the minimization of construction carbon footprint in the UK. A questionnaire was developed based on the carbon reduction barrier variables identified through a com-prehensive literature review and pilot study. Then a survey was conducted amongst construction profes-sionals in the UK and the data obtained were analyzed using mean ranking technique and factor analysis. The mean ranking analysis gave rise to 15 critical barriers to carbon reduction and their factor analysis yielded four dissimilar factors which are, resources and prioritization; policy and standards; risk and com-mitment; and awareness and complexity. The findings imply that these classifications can be used to describe carbon reduction barriers in the UK. This could stimulate a better comprehension of the critical barriers and provide a foundational basis for further research within the sustainable construction domain. Also, the result of this study could facilitate the need for collaboration between construction stakeholders in the UK to proffer pragmatic solutions to the identified challenges of carbon minimization.

The fragmented nature of construction operations makes it challenging to implement carbon-reduction strategies. However, attaining a holistic construction sector decarbonisation hinges upon ensuring all aspects of a construction project’s lifecycle are decarbonised, including the construction process stage. Therefore, to mitigate the implementation challenge of reducing the levels of carbon involved in construction processes, this study attempts to synthesise and categorise carbon-reduction strategies that could be employed to decrease the carbon footprint during a construction projects’ delivery. To achieve the aim of this study, a systematic literature review approach was adopted. Based on this technique, a total of 26 relevant articles within the built environment research area were eligible for the study, and their analysis revealed 56 carbon minimisation measures, which were summarised into nine distinct categories to ease their application and overcome the construction operations’ complexities. The nine categories include material transport, waste transport, materials and equipment, waste, materials, on-site office, on-site lighting, on-site transportation of material and equipment, and construction methods. The findings of this study provide contractors with a suite of measures that can be deployed to reduce the carbon impact of construction project delivery.

The catastrophic effect of fire incidents such as loss of lives, damage to building structures and economic loss, underscore the need for efficient fire safety in buildings, which has been a major subject of discussion in the UK. In this study, a comprehensive review of literature pertinent to building fire safety in the UK is presented. The study adopts systematic review approach, collected data from Scopus and analysed 51 qualified articles quantitively and qualitatively. The review shows a rise in publication since 2004, revealing prominent authors and keywords in building fire safety research. The review further identified the categories of fire safety practices in the UK, including technological innovations, mitigation, behavioural, and regulatory measures. Notable findings reveal the challenges in current practices including compliance and enforcement issues, maintenance of fire safety systems, public awareness and behavioural issues, technological adoption and integration challenges, and infrastructure and building design challenges. To address the challenges identified, proposed recommendations include fire safety training, simplifying and unifying regulations, maintenance and inspection of fire safety systems, fostering and upholding public trust, enhancing public awareness, integration of advanced technologies, and formulation of fire safety strategies. Additionally. the study further recommends more comparative research on international fire safety practices and social factors influence on fire regulations to effectively enhance fire safety practices in the UK.

International Sustainable Ecological Engineering Design for Society (SEEDS) Conference Proceedings2025Conference theme: Pursuing sustainability from an auxiliary commitment to a core priority, beyond common-sense, in a changing political and socio-economic landscape.Across previous years, our understanding of the influence of the socio-economic characteristics on sustainable practices has seen a significant advancement. However, the political characteristics contribute little to our understanding of sustainable behaviour amidst change in national policies, housing, contribution to the natural environment, use of innovations, value for money, and overall impact of power within relative markets. For some, the impact of any change on the political landscape would ultimately mean an impact on the adoption of sustainable solutions, while for others, such a change does not necessarily entail a primary course of action.Amidst a change of political agendas, we now yet again find ourselves overlooking various scenarios that challenge the core tenets of Sustainable Ecological Engineering Design for Society (SEEDS). Without delving into the plausible adverse effects of political decisions, and often also the indecisions, we stand to offer a little more than applied commonsense, as commonsense to promote these tenets may now need to be crafted in a slightly distinctive way.This year we ask...How do we redefine the ways we convey sustainability and ecological engineering, from a rather often seen auxiliary solutions to fit within the core values of any upcoming change?This question will be critically investigated in this conference by trying to reach sensible answers to some of the complex questions, for, as Thomas Sowell’s famous quote in A Conflict of Visions, “There are no solutions. There are only trade-offs”. It is indeed healthy to have scepticism toward one-size-fits-all solutions, and to embrace the complexity of decision-making.To advance these arguments, the conference themes will encompass:Socio-economic Influence on Sustainability: The impact of socio-economic characteristics on sustainable practices and behaviours.Global and Local Political Agendas: The shifts in political agendas and their role in challenging existing policies, housing, ecological engineering, and market powers on sustainability adoption.Political Decisions and Indecisions: Adverse effects of political actions and inactions on ecological engineering and sustainability.Contextual Sustainability Narratives: Narratives that align longstanding sustainability goals with existing political priorities.Interdisciplinary Exploration of Sustainability: Sustainability and ecological engineering through diverse perspectives and tailored approaches.

Conference theme: Decarbonise or Demise - implementing zero carbon now. This year we ask:What must we do now to enable transformation towards carbon conscious communities; enabling a resilient carbon neutral future?The International SEEDS Conference aims to bring together experts from around the world to focus on changes required to minimise the impact of human activity on the environment. This year, the conference invites papers aligned to the three main themes of the Suffolk Sustainability Institute. The themes are interdependent and resonate with the imperatives we face in addressing the climate crisis, whether through technological transformation, community collaboration or resource management – each theme addresses issues that we know are vital when changing and challenging practice within our built environment. The arguments are well rehearsed, and this year we have witnessed another COP meeting. SEEDS provides an opportunity for us to share knowledge and experience to implement the research and evidence-based change needed within society to respond to and address ecological and climate impacts.Three main conference themes:Blue/Green Infrastructure (Technology – Smart Solutions for Future Smart Cities and Buildings, Data Science and IOT; Ecology – Nature Based Solutions, Ecosystems Services, Natural Systems, Biodiversity Net Gain)Sustainable Healthy Communities (Community Engagement; Smart Healthy Living/Health and Wellbeing; Behavioural Science to embed Change)Energy and Resource Management (Energy Efficiency and Sustainable Production; Materials Engineering within the Built Environment; Resource Efficient solution and a Circular Economy)

Conference Theme: United Nation’s Sustainable Development GoalsIt has been thirty-five years since the seminal report Our Common Future (often referred to as the Brundtland Report), published by the World Commission on Environment and Development, proposed progressive environmental strategies and recommendations towards the attainment of the United Nations “A Global Agenda for Change”. Since then, we have seen many pivotal international actions, summits, frameworks, protocols, conventions, and declarations proposed. Amongst the most important was the Millennium Development Goals (MDGs), established by the United Nations in 2000, with 191 nations committing towards their delivery by 2015. Moving forward, the United Nations “2030 Agenda for Sustainable Development”, adopted by all Member States in 2015, proposed a shared blueprint for peace and prosperity for people and the planet, both now and into the future. At its core are the Sustainable Development Goals (SDGs), which are an urgent call for action by all countries to join in a global partnership towards their achievement. We are almost mid-way through the timeframe for the delivery of the seventeen SDGs so it is apt that society takes an opportunity to reflect on many of the triumphs towards their realisation and also seek steer on the directions of their future travel.Drawing on the themes of all the SDGs, the 2022 International Sustainable Ecological Engineering Design for Society (SEEDS) conference offers an integrated platform for academics and practitioners to reveal their personal and collective contributions towards the seventeen priorities.

Purpose: This study aims to examine facility management practices in the Nigerian healthcare sector, exploring approaches and identifying challenges facing effective healthcare facilities management. The purpose is to contribute to the development of a framework for enhancing healthcare facility management efficiency in Nigeria. Design/methodology/approach: The study employs a sequential in-depth exploratory qualitative research approach. The data collection involved conducting semi-structured interviews with 15 facility managers from diverse healthcare organisations in Nigeria. The qualitative data collected were analysed using thematic analysis. Findings: The study reveals scheduled, unscheduled and mixed approaches as the three facility management approaches used in Nigeria. It also substantiates the underdeveloped nature of facility management in Nigeria's healthcare sector, exacerbated by challenges such as socioeconomic, operational, technological and regulatory challenges. Practical implications: The study uncovers systemic issues affecting have attainment of Sustainable Development Goal 3 (Good Health and Well-being) and advocates for a comprehensive approach to enhance healthcare infrastructure, contributing to improved health outcomes and sustainable development. Originality/value: This research uniquely uncovers the hidden challenges facing effective healthcare facility management in Nigeria, providing a foundation for stakeholders to formulate solutions and rescue the struggling state of healthcare facilities in the country.

Twenty-first-century literature on projects has stressed concern for understanding project management as the management of uncertainty. This perspective is informed by two fields within normative project management, those of risk management and the management of complexity. Such perspectives and these responses for practice conform to the ‘control paradigm’. Related to this control, and firmly established in normative project management, is the so-called iron triangle which links the project management challenge to first set and then stick to budgets of time, cost and ‘quality’. Problems with this interpretation of the iron triangle of constraints have seen adaptations being proposed, such as performance, client and stakeholder satisfaction, value, learning and more recently under the structuring of ‘programme management’, time, cost and benefits. This paper proposes to address the relevance of the current theory of project constraints specifically relating to the iron triangle through the lens of an inductive cross-case analysis of nine past megaprojects. These variables add a whole new dimension of uncertainty and accompanying complexity to the project challenge including the project success rates of projects. The underlying theory of the iron law for megaprojects would therefore appear to be obsolete. An introduction of a more ‘contemporary iron triangle’ as a performance measurement tool is made. With megaprojects continued to be regarded, a multi-trillion-dollar global delivery model, the magnitude of influence mega-projects exert on society necessitates the theory of project management to encompass changing perceptions of success over longer time frames. Whilst for more obvious mega-projects, this might be a more useful interpretation for all projects, arguably project management’s thornier test.

Digitalisation in the construction sector promises numerous benefits, yet its widespread adoption in India faces impediments. This study aims to identify and analyse critical barriers hindering the adoption of digitalisation within the Indian construction sector. The study adopts a multi-phase research approach, involving a literature review to identify potential barriers. Subsequently, a questionnaire was administered to 162 professionals in the Indian construction industry. The survey reveals a willingness within the Indian construction sector to embrace digitalisation, citing benefits such as enhanced productivity and revolutionary impacts on construction processes. Larger organizations exhibit greater proactivity, while smaller businesses face challenges in resources and knowledge, leading to slower adoption rates. The study identifies five key themes of barriers, including financial/resource constraints, cultural/organisational constraints, regional disparities, data security/privacy concerns, and awareness/capacity-building constraints. The implication of the study is to unveil crucial barriers and provide insights for tailored interventions, aiding stakeholders, policymakers, and researchers in navigating the evolving digital landscape of the Indian construction industry. This study contributes to the exploration of perceptions of construction professionals specifically in India on why digitalisation is not fully embraced in the Indian construction sector.

Purpose Notwithstanding the Geographical Information System (GIS) being a fast-emerging green area of a digital revolution, the available studies focus on different subject areas of application in the construction industry, with no study that clarifies its knowledge strands. Hence, this systematic review analyses GIS core area of application, its system integration patterns, challenges, and future directions in the construction industry. Design/methodology/approach A systematic review approach was employed, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. A total of 60 articles published between 2011 and 2022 were identified, thoroughly reviewed, and analysed using thematic analysis. Findings The analysis revealed spatial planning and design, construction-task tracking, defect detection, and safety monitoring as its four main application-based areas. The findings showed that the adoption of GIS technology is rapidly expanding and being utilised more in building projects to visual-track construction activities. The review discovered an integrated pattern involving data flow from a device and window-form application to GIS, the pathways to data exchange between platforms-to-platforms, where ArcGIS is the most used software. Furthermore, the study highlighted the lack of interoperability between heterogeneous systems as the crux impediment to adopting GIS in the built environment. Originality/value The research provides a deep insight into possible areas where GIS is adopted in the construction industry, identifying areas of extensive and limited application coverage over one decade. Besides, it demystifies possible pathways for future integration opportunities of GIS with other emerging technologies within the construction industry.

Existing research on public attitudes towards fire safety and evacuation has predominantly employed traditional methodologies. While these methods have provided valuable insights, they are limited in their ability to capture the full spectrum of public. Moreover, a significant gap exists in the literature regarding the broader population's concerns of how the general public perceives and responds to building safety regulations, particularly in the context of new legislation like the Building Safety Act (BSA) 2022. To address these gaps, this study adopts a novel approach by analysing social media data, specifically YouTube, to capture a wider range of public sentiments towards the BSA 2022. A total of 3577 data points reflecting the general public's views were gathered, processed, and examined using sentiment analysis, k-means clustering and Latent Dirichlet Allocation text mining techniques for topic modelling. Findings reveal nine clusters each for the positive and negative sentiments. The overall findings reveal that the public expressed positive sentiments (20%), negative sentiments (4%), and neutral sentiments (76%) towards (Building Safety Act, 2022). The study posits recommendations from the public’s sentiments for policy makers to leverage on.

Purpose: Amidst all solutions posited to address sustainable construction practices in Nigeria, the implementation plans are repudiated by sustainable barriers. This study examines and confirms the strategy with the most significant impacts on the identified barrier to Sustainable Construction Practice (SCP). Design/Methodology/Approach: The study deployed a questionnaire survey to evaluate the perspective of 100 construction actors on the barriers and strategies of sustainable construction practice in Nigeria. Factor Analysis was employed to categorize key barriers and strategies into their underlying clusters for further analysis. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to confirm the constructs significant relationship and magnitude, thereby establishing the strategies with the highest impacts on the barriers to sustainable construction practices. Findings: The findings revealed three clusters of barriers and four groups of strategies to SCP, including techno-politic barrier, perception and awareness barrier, and sociocultural barrier. For the significant strategies, education and training, stakeholder regulation, incentive support and government and legislative support strategies were established. Overall, education and training strategy was identified as the most dominant and effective strategy to mitigate the barriers of SCP in Nigeria. Originality/Value: The paper establishes education and training as the key strategy to achieving sustainable quest in the AEC industry. The practical implication is that policy makers, educators and professional bodies can harness sustainable knowledge transfer through education and training to improve sustainable construction practices in Nigeria.

Purpose Housing adaptation enables the elderly and disabled people to live healthy, productive, independent, and dignified lives by making it easier to carry out everyday tasks in a safer environment. Assistive technology enables them to perform tasks that would otherwise be difficult or impossible to achieve independence. Therefore, the new guidance for local authorities in England on Disabled Facilities Grants (DFG) recommends assistive and smart technology as part of a DFG award package to maximise the benefits of home adaptations. Through a baseline study, this paper examines the disabled and elderly peoples’ level of digital engagement, the challenges and the impact of smart devices on their daily living and well-being. Design/methodology/approach A baseline study approach was used through the co-production of knowledge. The study relies on a questionnaire survey and the Most Significant Change (MSC) research technique through structured interviews. A percentage of the total approach is used in analysing the quantitative data by comparing the pre- and post-baseline results. Findings The major challenges the clients face are Wi-Fi connectivity, system compatibility issues, and skills in navigating different applications and operating systems. The pilot study demonstrates that assistive technology supports independent and healthy living and promotes a secure and safe environment. It also promotes social inclusion and improves the elderly and disabled people’s comfort and engagement. However, the results show that the overall impact is minimal; therefore, a longitudinal study is needed to understand the impacts over a long period. Practical implications The findings will help policymakers to understand the impacts of assistive technology on improving the elderly and disabled people’s quality of life. Originality/value A comparable baseline study was carried out to serve as a reference point for other similar future studies. The paper also highlights the challenges faced by the elderly and disabled people, their digital skill level and the impact of smart devices on their daily living and well-being.

Purpose: This study investigates the various competencies a graduate should hold to prepare them for graduate building surveying roles from employers’ perspective. Design/Methodology: The study employs a sequential exploratory mixed-method approach by informing a quantitative study with the finding from a qualitative study. Findings: Based on exploratory factor analysis, the study found that 13 essential competencies are valued by the employers when recruiting building surveying graduates, as they are requisites for effective job performance. Personal management skills, technical surveying knowledge, and knowledge of RICS standards are the essential competencies based on the level of variance extracted by the three components. Other competency categories include client management skills, being goal-driven and self-motivated, optimistic personality traits, strong mental resilience, building maintenance and management knowledge, and time management skills, among others that are explained in the paper. Originality/Value: The essential competencies were dependent on maintaining a balance between knowledge, skills and personality-based competencies. Measures and approaches for gaining the essential competencies, as well as their level of significance, are further discussed. The study will be of significant benefits to employers of graduate building surveyors, academic institutions that are seeking to improve their graduate employability, as well as students that are preparing for the world of work.

Purpose Digital uptake amongst construction organisations is described as slow and ineffective, undermining a fundamental transformation and limiting construction firms from exploiting the digital benefits. In this space, meaningful research that utilises a qualitative approach in pursuit for employees' insights towards digital transformation is lacking. Such limited focus from previous efforts presents an opportunity to illuminate the determinants of an effective digital transformation that are, arguably, responsible for the status quo of low digital uptake in the construction sector. This paper aims to discuss the aforementioned idea. Design/methodology/approach This study adopts a qualitative approach to address the literature's digital discreetness in construction. The qualitative approach captures employees' perspectives through its unbounded characteristic of encouraging illustration and discussion. Findings This paper captures 35 digital transformation determinants under three clusters, namely, organisation related, i.e. hierarchy, size and management, people related, i.e. team orientation, training and knowledge and leadership related, i.e. awareness, attitude, approach and leaders' characteristics. Findings suggest a new set of arguments in relation to understudied factors and their influence on the digital uptake in construction organisations. Originality/value This paper offers empirical indication of the determinants believed to influence an effective digital transformation in construction organisations. Such conceptualisation is crucial and is depicted as perceived by construction employees and practitioners, which is a less biased approach than that of comparable studies that argue the viewpoints of industry leaders in isolation of other members of the hierarchy.

Purpose Extant studies have discussed numerous carbon reduction drivers, but there is a dearth of holistic review and understanding of the dynamic interrelationships between the drivers from a system perspective. Thus, this study aims to bridge that gap. Design/methodology/approach The study conducted a review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses and adopted interpretive structural modelling (ISM) to analyse and prioritise the drivers. Findings Eighteen drivers were identified and grouped into five, namely, policy instruments, bid-related, cost and risk, education and training, and reward and penalty drivers. The ISM revealed two hierarchical levels of the drivers with only higher cost of electricity/fuel on the higher level, making it the most important driver that could influence others. Practical implications The study presents an overview of decarbonisation drivers in the literature and would be of benefit to the government and stakeholders towards achieving net zero emissions in the construction industry. Originality/value The findings of the study present drivers of carbon reduction and prioritise and categorise them for tailored interventions within the construction sector. Also, it could serve as foundational knowledge for further study in the construction process decarbonisation research area.

The construction industry’s operations heavily contribute almost half of the UK’s carbon emissions and reportedly falling behind in achieving the net zero targets set out by the Paris Agreement. This necessitates a deeper understanding of the main barriers to achieving net zero in the construction industry. As such, this paper aims to gain an understanding of the barriers to procuring and building net zero assets through the lenses of the construction industry and the built environment’s interdisciplinary workforces. The study adopted an exploratory qualitative research approach involving the use of semi structured interviews conducted for 13 professionals working across a range of sectors, different sizes of organisations in the construction industry and built environment with varying levels of experience. The factors and discussions taken from the interview transcriptions were collected and subjected to thematic analysis using the K-Mean clustering learning algorithm and Latent Dirichlet Allocation topic modelling. The analysis produces thirty-two distinct factors clustered/modelled into five themes/topics referred to as barriers to net zero adoption in the UK construction sector. These barriers are financial and economic barriers, technological and expertise challenges, regulatory and policy issues, industry practices and cultural resistance, and supply chain and operational issues. The findings of this research indicated that net-zero construction is usually seen as inherently expensive, with financial budgets often being prioritised ahead of net-zero goals. Thus, financial support and incentives from the government are required to alleviate the high costs associated with net zero technologies, particularly for smaller organizations to drive the adoption of net zero practices.

Purpose The decisions of contractors could impact the reduction of construction carbon footprint. These decisions are linked to the belief of contractors which equally affects how they behave while delivering projects. This study aims to investigate the behavioural tendencies of contractors that could lead to carbon minimisation during the execution of construction projects. Design/methodology/approach An industry survey was performed amongst 41 UK construction professionals. Spearman’s correlation and factor analysis were used to analyse the data. Findings The result of the Spearman’s correlation gave rise to 14 contractors’ carbon reduction behaviour (CCRB) variables and their factor analysis yielded two distinct factors, namely, contractors’ consummate carbon reduction behaviour and contractors’ pragmatic carbon reduction behaviour. The findings suggest that in the UK, contractors are willing to take voluntary practical steps to decrease the carbon footprint of construction projects. Practical implications This finding might be unexpected to construction stakeholders, especially construction clients who may believe that infusing strict carbon reduction obligations in contracts is sufficient in nudging contractors to lessen the carbon impact of projects. Originality/value The study attempted to quantitatively derive CCRB, thereby extending the breadth of knowledge in the construction carbon reduction domain.

Automated Compliance Checking (ACC) is continuously gaining traction in improving the efficiency and precision in regulatory compliances within the AEC sector. Thus, this research presents a comprehensive review of the current state of ACC emphasising its application domains, techniques, challenges and opportunities. The review reveal that ACC is currently being applied in multiple domains including building design analysis, energy efficiency, construction safety and fire safety. ACC systems currently employ techniques such as artificial intelligence (machine learning, neural networks, and natural language processing), graph-based methods, semantic enrichment and representation and general rule representation analysis. The review identifies technological constraints and integration difficulties as main challenges facing ACC implementation. The potential opportunities for ACC include integration with enhanced technologies, expanding application domains, collaborations and standardisations. This study addresses existing knowledge gaps and enhances the understanding of ACC's role and impact, steering future research towards innovative approaches and improved implementation strategies.

Existing research on constructional professional attitudes towards fire safety and evacuation has predominantly employed traditional methodologies. While these methods have provided valuable insights, they are limited in their ability to capture the full spectrum of the stakeholders. Moreover, a significant gap exists in the literature regarding the broader population’s concerns about how the industry experts perceives and responds to building safety regulations, particularly in the context of new legislation like the Building Safety Act (BSA) 2022. To address these gaps, this study adopts a novel approach by analysing social media data, specifically YouTube, to capture a wider range of public sentiments towards the BSA 2022. A total of 3577 data points reflecting the general public’s views were gathered, processed, and examined using sentiment analysis, k-means clustering and Latent Dirichlet Allocation text mining techniques for topic modelling. Findings reveal nine clusters each for the positive and negative sentiments. The overall findings reveal that the public expressed positive sentiments (20 %), negative sentiments (4 %), and neutral sentiments (76 %) towards BSA 2022. The study posits recommendations from the public’s sentiments for policy makers to leverage.

Due to the practical complexity and fragmented nature of the construction industry, digitalisation, like other innovations, is not easily achieved. This study aimed to explore organisational influences on digitalisation within construction firms. Due to the practical complexity and fragmented nature of the construction industry, digitalisation, like other innovations, is not easily achieved. This study aimed to explore organisational influences on digitalisation within construction firms. Construction professionals from twenty-two organisations provided their perspectives on digital transformation and their organisations. Under four constructs- structure, culture, leadership, and internal processes, findings uncovered sixteen determinants critical to digitalisation in construction firms. The study offers a offers a theoretical perspective supported by empirical data to explore the complex dynamics and internal interactions of organisational influence on the uptake of digitalisation in the construction industry. This paper offers arguments from a theoretical lens by applying the organisational influence model and capturing the variables under each construct in an exploratory manner to highlight the reasoning behind the low digital uptake in construction firms. This research aids academia and practice on the pressure points responsible for enhancing, or undermining, digital uptake in construction firms at an organisational level.

Purpose – In the past decade, transforming key processes and activities towards a more digital nature has been the focus of most industries due to the associated advantages. Despite that, organisations in the construction sector are lagging the list of early adopters. The slow rate of a fundamental digital transformation is linked to the challenges facing an effective leadership. The purpose of this paper is, therefore, to shed light on the barriers to digital leadership enactment in the construction industry. Limited research has empirically analysed and discussed these barriers to explain the low transformation rate in the existing body of knowledge. Design/methodology/approach – This paper empirically investigates the perspectives of construction industry professionals acquiring various roles in the industry. Overall, the study comprises the views of 38 participants, adopting a qualitative methodological approach to explore relative barriers and explain the slow digital transformation rate. Findings – Findings are grouped into five themes: leadership characteristics, management and organisational issues, resource constraints, technological issues, and risk perceptions. The findings are helpful to business leaders, researchers, trainers, and educators to develop measures to encourage leaders in the industry to be at the forefront of digital transformation in their organisations. Originality/value – Literature, however, is discreet in reflecting the challenges and barriers facing today’s leadership in facilitating digital transformation among construction stakeholders. This paper provides insights into the variables that may be undermining wider digital adoption across the construction sector’s organisations.