Hadi Kazemi

Increasing global competition on product quality and production costs, and the need for flexibility in production petition for transformed production processes which enable high level of connectivity and integration between business processes and systems. Much of the conventional computer- integrated efforts and advanced manufacturing technologies are limited in scope and restricted to only some organisational areas. Such limited scope, which stems from limited connectivity and integration between manufacturing and enterprise systems, confines the achievement of full potential of these systems within manufacturing. Industry 4.0, characterised by computing developments, can create a platform for addressing integration challenge through enabling comprehensive connectivity. Hence, this paper, through following deductive research paradigm and using systems theory as the theoretical base, aims to investigate recent academic research and industrial reports in the area of Industry 4.0 and smart manufacturing to provide detailed insights on execution of Industry 4.0, and to propose a theoretical framework for operationalisation of Industry 4.0 in manufacturing.

Despite growing attention to equality, diversity and inclusion (EDI), leadership roles in the UK construction and railway sectors remain disproportionately inaccessible to individuals from Black, Asian and Minority Ethnic (BAME) backgrounds. This study explores the systemic, cultural, and structural barriers affecting leadership diversity through a qualitative investigation involving seven semi-structured interviews with professionals from these sectors. Thematic analysis identified two primary themes (Career Development Mechanism and Barriers; Structural Barriers to Inclusion) and six subthemes: Representation Disparities, Organisational Culture Fit, Unconscious Bias and Manifestations, Sponsorship Dynamics, Mentorship Availability, Professional Development Support. The findings illustrate that leadership progression for ethnic minorities is hindered by embedded biases, a lack of inclusive networks, and performative diversity strategies. The study highlights the need for authentic allyship, leadership sponsorship, and institutional accountability. By contextualising these challenges within the broader theoretical frameworks of implicit leadership theory and social dominance theory, the research offers practical and policy-oriented recommendations to advance diversity in leadership. These insights contribute to the growing discourse on race, representation, and leadership equity in UK infrastructure industries.

This study explores the integration of artificial intelligence (AI) and blockchain technologies within secure digital frameworks to enhance emissions tracking and data integrity in cold chain logistics. Using Company A's extended supply chain as a case study, the research applies the Scope 3 Greenhouse Gas (GHG) Protocol to quantify indirect emissions across transportation, production, and storage activities. Findings indicate that downstream transportation is the largest emissions contributor, followed by inefficiencies in production and storage. The methodology combines quantitative data from Company A's operational records—including transportation logs, refrigeration efficiency, and supplier emissions—with external datasets such as the Carbon Cloud database. AI-driven predictive analytics, alongside a linear regression model, identify key emissions drivers such as transportation distance and refrigeration energy consumption. Additionally, blockchain enhances data integrity through cryptographic hash functions that secure real-time emissions records. Optimization algorithms further reduce emissions by refining delivery routes and improving refrigeration efficiency. Grounded in the Technology-Organization-Environment (TOE) Framework, Institutional Theory, and Dynamic Capabilities Theory, the findings underscore the strategic value of integrating AI and blockchain for real-time emissions monitoring, operational optimization, and regulatory compliance. This research provides actionable insights into scalable emissions management frameworks, offering a transformative approach to reducing environmental impact and aligning cold chain logistics operations with sustainability goals.

Over the years Corporate Social Responsibility (CSR) has been studied and implemented as an essential practice in many organizations being reflective of a growing corporate environmental, economic, and social conscience. In addition to the acknowledged benefits of increased company performance and its use as a strategic marketing approach (Albus and Ro, 2016). As such, it has become a critical subject in the literature from a strategic perspective due to its benefits. . However, it’s application in the built environment is unknown and this study seeks to fill that gap by assessing the current state of research into CSR, along with a detailed analysis identifying the areas of study that have been exhausted and those that require further investigation. This study will attempt to expose what is missing from current research to effectively guide future research of CSR within the built environment. The study adopted a quantitative strategy using a bibliometric approach to identify and analyse papers in the field of CSR in the built environment using Scopus abstract and citation databases from 1957 to 2022. The findings revealed a limited endeavour by academics to understand CRS within the built environment until the early 2000’s where it began to grow in popularity as a research area. . . This suggests CRS is still at a nescient stage which provides opportunity for further investigation in the areas identified The implications of this research are that areas of Corporate Social Responsibility of the built environment that have not been sufficiently studied can be identified and suggestions made for further investigation.

This study explores the adoption of smart technologies in sustainable logistics and mobility systems, focusing on the operational and environmental impacts, barriers to adoption, and key facilitators. The research is grounded in two prominent theoretical frameworks: the Technology Acceptance Model (TAM) and institutional theory. These frameworks provide a comprehensive lens to examine both internal organisational factors (e.g. perceived usefulness and ease of use) and external institutional pressures (e.g. regulatory mandates and industry norms) that influence the adoption of technologies such as IoT, Big Data Analytics, AI/Machine Learning, blockchain, and Electric Vehicles (EVs). Using a survey-based methodology, data were collected from organisations within the logistics and mobility sectors. The findings reveal that IoT and Big Data Analytics are the most widely adopted technologies, driven by their perceived operational benefits and ease of use. However, adoption of AI/Machine Learning, blockchain, and EVs remains constrained by high costs and infrastructural limitations, particularly for small and medium-sized enterprises (SMEs). Regulatory incentives, stakeholder collaboration, and public-private partnerships were identified as key facilitators of adoption, highlighting the importance of addressing both financial and technical barriers.

In this research, asphalt concrete (AC) mixtures modified by polymerized Sulfur were prepared. PG58-22 bitumen was used as the base binder for the mixtures along with crushed siliceous aggregate. The base binder was replaced by 20%, 30%, and 50% ratios with polymerized Sulfur in the modified mixtures while the reference mix was fabricated with 0% binder replacement. Single edge notched-beam fracture tests (SE(B)) were carried out in a temperature range of 0 °C to -20 °C on the AC beam specimens. Load-displacement curves were obtained from the experiments and the fracture energy of the mixtures could be determined. It was revealed that modifying the mixtures with polymerized Sulfur could improve the load bearing of the beam specimens as higher peak load values were recorded at fracture. However, fracture failure of the AC beams occurred at lower values of displacement addressing further embrittlement of the mixtures due to replacement of the base binder. Higher contents of polymerized Sulfur in the mixtures resulted in higher magnitudes of fracture energy as a general trend in this research addressing an improved resistance to low-temperature cracking.

© 2020 Informa UK Limited, trading as Taylor & Francis Group. Considering the crucial role Information Technology (IT) plays in achieving performance improvements in business processes, this paper aims to explore the potential impact of the fourth industrial revolution–Industry 4.0 and its associated technological advances on Supply Chain (SC) performance. This study is exploratory research, conducted based on inductive reasoning, which aims to bring new insights into the topic, and to provide forward-thinking for future research. Hence, through conducting a systematic literature review, the paper attempts to explore the impact of Industry 4.0 on SC performance and to conceptualise and develop findings into an operational framework underpinned by Systems Theory. Based on this research, the application of Industry 4.0-enabling-technologies is expected to bring about significant performance improvements in SCM by enabling a holistic approach towards supply chain management resulting from extensive supply chain integration as well as information sharing and transparency throughout the supply chain. Moreover, these technologies allow for huge performance improvements within individual supply chain processes such as procurement, production, inventory management and retailing through enabling process integration, digitisation and automation, and bringing about novel analytical capabilities.

The increasing complexity of urban logistics in smart cities requires innovative solutions that leverage real-time data, predictive analytics, and adaptive learning to enhance efficiency. This study presents a predictive analytics framework integrating digital twin technology, IoT-enabled logistics data, and cybernetic feedback loops to improve last-mile delivery accuracy, congestion management, and sustainability in smart cities. Grounded in Systems Theory and Cybernetic Theory, the framework models urban logistics as an interconnected network, where real-time IoT data enable dynamic routing, demand forecasting, and self-regulating logistics operations. By incorporating machine learning-driven predictive analytics, the study demonstrates how AI-powered logistics optimization can enhance urban freight mobility. The cybernetic feedback mechanism further improves adaptive decision-making and operational resilience, allowing logistics networks to respond dynamically to changing urban conditions. The findings provide valuable insights for logistics managers, smart city policymakers, and urban planners, highlighting how AI-driven logistics strategies can reduce congestion, enhance sustainability, and optimize delivery performance. The study also contributes to logistics and smart city research by integrating digital twins with adaptive analytics, addressing gaps in dynamic, feedback-driven logistics models.

This study investigates how Industry 4.0 technologies can optimize transportation efficiency and contribute to global sustainability goals by reducing CO2 emissions. In response to the pressing climate emergency, the research examines the role of the Internet of Things (IoT), Artificial Intelligence (AI), and predictive analytics in enhancing operational performance and aligning transportation systems with Sustainable Development Goals (SDGs), particularly Goal 13 (climate action) and Goal 9 (industry, innovation, and infrastructure). Using a qualitative research approach, semi-structured interviews and focus groups were conducted with industry experts, and the data were analyzed using thematic analysis and qualitative network mapping in NVivo software. The findings reveal that IoT enhances real-time monitoring, AI enables dynamic route optimization, and predictive analytics supports proactive maintenance, collectively achieving an average emission reductions of 30%. However, adoption is hindered by infrastructure gaps, high implementation costs, skill shortages, and fragmented regulatory frameworks. This study integrates the Technology–Organization–Environment (TOE) framework and Sustainable Corporate Theory to provide a structured analysis of digital transformation in transportation. The findings offer strategic insights for policymakers and industry stakeholders, highlighting the need for stronger regulatory support, targeted incentives, and digital infrastructure investments.

This study explores how digital technologies and data analytics can transform urban waste management in smart cities by addressing systemic inefficiencies. Integrating perspectives from the Resource-Based View, Socio-Technical Systems Theory, Circular Economy Theory, and Institutional Theory, the research examines sustainability, operational efficiency, and resilience in extended supply chains. A case study of Company A and its demand-side supply chain with Retailer B highlights key drivers of waste, including overstocking, inventory mismanagement, and inefficiencies in transportation and promotional activities. Using a mixed-methods approach, the study combines quantitative analysis of operational data with advanced statistical techniques and machine learning models. Key data sources include inventory records, sales forecasts, promotional activities, waste logs, and IoT sensor data collected over a two-year period. Machine learning techniques were employed to uncover complex, non-linear relationships between waste drivers and waste generation. A waste-type-specific emissions framework was used to assess environmental impacts, while IoT-enabled optimization algorithms helped improve logistics efficiency and reduce waste collection costs. Our findings indicate that the adoption of IoT and AI technologies significantly reduced waste by enhancing inventory control, optimizing transportation, and improving supply chain coordination. These digital innovations also align with circular economy principles by minimizing resource consumption and emissions, contributing to broader sustainability and resilience goals in urban environments. The study underscores the importance of integrating digital solutions into waste management strategies to foster more sustainable and efficient urban supply chains. While the research is particularly relevant to the food production and retail sectors, it also provides valuable insights for policymakers, urban planners, and supply chain stakeholders. By bridging theoretical frameworks with practical applications, this study demonstrates the potential of digital technologies to drive sustainability and resilience in smart cities.

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

The surge in popularity of on-demand transportation services, fueled by advancements in technology and changing urban mobility patterns, has significantly reshaped urban transportation dynamics. This transformation presents challenges to traditional public transportation, raising questions about sustainability and energy efficiency. This research addresses these challenges through an explorative literature review, focusing on operational efficiency, energy transition, and policy implications. By synthesizing and analyzing existing literature, the study uncovers insights into on-demand transportation, identifies challenges and opportunities, and proposes avenues for further research. The study also develops operational and theoretical frameworks to support policy formulation and implementation in urban transportation planning, offering guidance for policymakers and urban planners. Ultimately, this research aims to contribute to developing evidence-based policies and practices that foster sustainable urban transportation networks.

Urbanization has led to significant traffic congestion, presenting challenges for traditional traffic management systems that rely on static and rule-based approaches. These systems struggle to adapt to real-time changes in traffic patterns, resulting in inefficiencies and delays. Intelligent Transportation Systems (ITS), leveraging advanced technologies such as sensors, communication networks, and data analytics, offer promising solutions. This study aims to develop and validate a conceptual framework integrating deep learning, reinforcement learning, and transfer learning into ITS for dynamic and adaptive traffic management. An explorative literature review identifies key constructs, including real-time data collection, data preprocessing, adaptive signal control, and predictive analytics. The framework is validated through case studies from Singapore, Los Angeles, and Rio de Janeiro, demonstrating practical implementation and impact. The findings highlight the potential of learning-based ITS solutions to enhance traffic flow, reduce congestion, and improve urban transportation networks, contributing to the broader vision of smart cities.

Fracture resistance curves (R-curves) have served as a robust tool in characterizing the entire fracture process of engineering materials. However, obtaining such curves for asphalt concrete (AC) mixtures is cumbersome due to the non-linear inelastic behavior of the mixtures. In this research, a singlespecimen technique is developed based on the unloading compliance method which is used for metals. AC mixtures with limestone aggregate and PG58-22 binder were prepared. Beam specimens were fabricated and single-edge notched beam (SE(B)) fracture testing was conducted at low temperatures. A loading-partial unloading regime was used in the experiments and crack growth increments were captured by digital images throughout the tests. Using a multi-variable regression analysis, modified compliance equations were obtained for AC and R-curves of the mixtures could be constructed. It was revealed that the R-curve developed by ASTM E1820 compliance method could potentially overestimate the resistance of the mixtures against low-temperature fracture. The constructed R-curve exhibits a lower semi-vertical region addressing lower resistance of the mixture in the crack blunting phase. Also, the post-peak phase of the fracture shows a significantly lower slope in the constructed R-curve which denotes lower resistance of the mixture against unstable crack propagation.

This study explores how logistics optimisation can accelerate the transition from linear to circular supply chains, supporting global sustainability goals. Grounded in closed-loop supply chain (CLSC) theory, it investigates how advanced logistics models—such as route planning algorithms, fuel consumption prediction, and emission reduction techniques—can enhance resource efficiency, reduce waste, and close material loops. The research combines five years of historical data on vehicle performance, routes, and fuel use with real-time information on traffic and weather, integrated through public APIs. Using regression analysis for fuel prediction, multi-criteria optimisation for balancing cost and emissions, and dynamic routing algorithms responsive to live conditions, the study identifies strategies for improving both efficiency and sustainability. Results reveal measurable reductions in fuel consumption, emissions, and logistics costs, demonstrating the value of data-driven optimisation in implementing circular economy (CE) practices. By addressing trade-offs between cost, performance, and environmental impact, this research provides actionable insights for organisations seeking to shift from traditional linear models toward more resilient and resource-efficient circular frameworks. Overall, it highlights the critical role of predictive analytics and optimisation tools in operationalising CLSC principles and advancing sustainable logistics management.

The increasing global demand for sustainable production practices has propelled green manufacturing to the forefront of industrial innovation. Central to this movement is the integration of smart sensors for real-time data collection, an approach that promises significant advancements in waste reduction and energy efficiency. This chapter explores how smart sensor technology can transform traditional manufacturing processes by enabling continuous monitoring, data-driven decision-making, and predictive maintenance. Through a comprehensive review of real-time monitoring applications, the chapter discusses various sensor types, their roles in capturing critical data on emissions, resource consumption, and waste outputs, and their integration into green manufacturing systems. Case studies from diverse industries illustrate the substantial impact of smart sensors in optimising energy use, reducing material waste, and supporting circular economy models. Further, the chapter examines the technical challenges and data privacy considerations associated with real-time monitoring and proposes solutions to enhance adoption. By leveraging intelligent data analytics and Internet of Things (IoT) connectivity, manufacturers can achieve a more responsive, eco-friendly production environment. This chapter concludes with a discussion on the future implications of smart sensor technology in green manufacturing, highlighting the role of regulatory standards and industry partnerships in driving innovation for a sustainable future.

The digital transformation of cold supply chains is gaining importance due to its potential to improve efficiency, reduce waste, and support global sustainability goals. This study examines the key barriers to adopting Industry 4.0 technologies in cold chain management. These issues are especially pronounced for small and medium-sized enterprises (SMEs) and organizations operating in resource-constrained regions. A qualitative research design was employed, using semi-structured interviews and focus groups with stakeholders involved in cold chain operations, technology development, and policymaking. Thematic and qualitative network analysis identified several interconnected obstacles, notably high upfront investment costs, insufficient technological infrastructure, fragmented regulatory environments, and resistance to organizational change. Financial constraints were found to be closely linked with technological limitations, collectively restricting the ability of firms to invest in digital upgrades. The study emphasizes the necessity of a holistic and coordinated response to these challenges. Recommended strategies include financial incentives and support schemes, targeted investments in digital infrastructure, regulatory harmonization, and workforce training initiatives. Overall, the research provides a structured roadmap for phased digital transformation in cold supply chains, offering practical guidance for stakeholders and policymakers. Addressing these interconnected barriers can enhance sustainability, operational resilience, and competitiveness amid increasing global supply chain pressures.

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.

As materials undergo large-scale yielding or exhibit large sizes of fracture process zone in the crack tip region, multi-parameter fracture concepts should be employed to describe the complex crack-tip stress-strain fields. Fracture resistance curves (R-curves) are an established tool in characterizing the entire fracture process of such materials. However, for complex materials such as bituminous mixtures, the development of these curves is subject to experimental and computational intricacies. In this research, a framework is developed to automate the construction of R-curves for normal and rubberized asphalt concrete (AC) mixtures. AC mixtures are produced using PG58–22 and PG58–28 binders. Limestone and siliceous aggregates are used, and three binder contents are considered for the mixtures. Single-edge notched beam (SE(B)) fracture testing is conducted on AC beams with two different notch patterns. A convolutional neural network (CNN) model is developed and trained over 1260 test images with varying temperatures, notch geometries, and setups. The CNN model was used to detect the growing crack on the beam surface and each crack-detected image was sent to an image processing framework to measure the crack length. Crack extension increments were calculated and synchronized with test time and magnitude of load, load-line displacement, and cumulative fracture energy, and the R-curve could be constructed. A training accuracy of 0.91 was obtained for the model and a loss of below 0.10 as a result of a hyperparameter tuning indicating reliable classifications by the CNN architecture. The R-curves showed desirable agreement for control mixtures at temperatures of 0 °C and −15 °C. As the mixtures are rubberized, the R-curves showed favorable agreement in the crack blunting phase, transition zone, as well as the unstable propagation phase at −20 °C. Cohesive energy magnitudes were compared for the two methods with a Pearson coefficient of 0.81 while fracture rate and fracture energy magnitudes showed favorably close magnitudes with coefficients of 0.89 and 0.98 respectively.

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.

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.