The construction industry continues to face high levels of accidents despite the use of various safety training approaches, highlighting the need for more effective and responsive methods. This study examines the role of Generative Artificial Intelligence (GenAI) in potentially improving construction safety training by exploring the development of training practices and identifying the shortcomings of existing approaches. A systematic literature review (SLR) was conducted to analyse safety training methods and emerging GenAI applications, followed by validation interviews with industry experts in South Australia to ensure practical relevance. Emergent findings show that safety training has progressed through three main stages: instructor-led, digital and GenAI-enabled. However, instructor-led and digital approaches remain limited by non-interactive learning, limited flexibility to different learner needs, lack of real-time feedback and weak alignment with actual site conditions. In contrast, GenAI offers opportunities to support more interactive, personalised and context-aware training through technologies such as large language models (LLMs), adaptive learning systems, computer vision and scenario generation. Despite these benefits, significant challenges related to data quality, system reliability, ethical concerns and organisational readiness continue to affect implementation. Based on these findings, the study develops an integrated framework that links training evolution, key challenges and GenAI capabilities, providing practical guidance to improve safety training in construction.

Purpose The construction industry is increasingly exploring the metaverse as a transformative digital paradigm to enhance collaboration, efficiency and project delivery. However, the current body of knowledge remains fragmented, lacking a comprehensive synthesis of adoption barriers, research trends and implementation pathways. This study aims to systematically investigate (1) the evolution and trends of metaverse-related research in construction, (2) key barriers to its adoption and (3) strategic pathways for its effective implementation. By integrating these dimensions, the study seeks to provide a holistic understanding of the metaverse ecosystem within the construction industry. Design/methodology/approach A systematic literature review was conducted following the PRISMA protocol to identify relevant studies from the Scopus database. A total of 34 records were selected through rigorous screening and snowballing. Scientometric analysis was employed to examine publication trends, collaboration networks and research hotspots. A novel hybrid multi-criteria decision-making (MCDM) framework, combining evaluation based on distance from average solution (EDAS) and criteria importance through intercriteria correlation (CRITIC), was developed to assess the quality and impact of the selected studies. Furthermore, barriers were extracted and analysed, and a conceptual framework for metaverse adoption was proposed. Findings The findings reveal a rapidly growing research interest in metaverse applications in construction, particularly after 2020. Key adoption barriers are categorised into political, economic, social, technological and cultural dimensions, with major challenges including high initial costs, lack of standardisation, regulatory uncertainty, technological immaturity and resistance to change. Interoperability issues, immature business models and limited awareness emerged as the most interconnected barriers. The proposed framework outlines a structured pathway from stakeholder awareness to industry-wide adoption, emphasising policy support, infrastructure development and technological integration. Originality/value This study contributes to the literature by integrating scientometric analysis, barrier identification, quality assessment and framework development within a unified analytical approach. Unlike prior studies that focus on isolated aspects, this research provides a comprehensive and systematic evaluation of metaverse adoption in construction. The findings offer actionable insights for policymakers, industry practitioners and researchers to facilitate strategic decision-making and accelerate digital transformation in the construction sector.

PurposeThe three-to-five-year wait for public housing in Hong Kong (HK) illustrates the housing shortage. The government has resorted to various approaches of transitional housing supplies. One of which is building transitional housing on vacant land as a temporary solution. Such housing facilities are built for a minimum of seven years or even less. When the hosting site is no longer accessible, the transitional housing is deconstructed and moved to a new available location to serve a second lifecycle. Ensuring circular economy (CE) adoption in transitional housing is essential to reduce construction and demolition waste and to promote circular transitional housing.Design/methodology/approachUsing institutional and innovation adoption theories as a foundation, this study identifies key institutional obstacles to CE adoption in transitional housing in HK. Major CE barriers from a review were refined through expert interviews. Collected questionnaire data from construction professionals were analysed.FindingsFindings revealed significant differences in underlying institutional barriers between the views of CE adopters and non-adopters. From the perspective of CE adopters, regulative barriers were the most critical, followed by normative barriers. However, from the non-adopters, normative barriers were paramount and then regulative barriers.Research limitations/implicationsThe study was conducted with a relatively small sample size (i.e., 51) due to difficulties in collecting responses to the questionnaire.Originality/valueUncovering the barriers is the first step to developing a viable solution for CE in transitional housing. The findings could inform decision-makers of general and tailored policies to expedite CE transition by innovators, early adopters and early majority and to begin CE implementation by the late majority and the laggards to promote circular transitional housing in HK and beyond.

Purpose Understanding how complex human, environmental, demographic and operational factors interact to elevate the probability of safety infractions is essential for developing predictive safety systems in high-risk environments such as highways. This study proposes a data-driven framework for identifying empirical inflection and threshold operational points, whereby risk shifts abruptly from acceptable to hazardous levels. In addition, this study develops interpretable, rule-based triggers for proactive safety interventions in highway maintenance and traffic management. Design/methodology/approach Using a synthetic dataset (backed by a positivist philosophical stance) reflecting safety-based variables recommended in the literature (e.g. human, operational, environmental stressors and organisational conditions), a supervised machine learning model (i.e. Random Forest) was trained to estimate infraction probabilities. A threshold discovery algorithm was then implemented, combining bin-wise probability estimation with prominence-based inflection detection and rule induction to extract applicable safety triggers. Feature importance measures were used to contextualise the relative influence of predictors on the model's risk output. Findings Results revealed clear, interpretable thresholds across multiple predictors, including sharp risk transitions for consecutive workdays (3–4 days), fatigue level (=5), sleep duration (<6 h), traffic density (>600 vehicles/hour) and physiological stress (>90 bpm). Non-linear variables such as cognitive load and training quality exhibited oscillatory risk patterns yet still produced meaningful inflection points. Based on the discovered thresholds, safety trigger rules were formulated to aid in safety management decision-making. Originality/value This study contributes a novel, transparent methodology for threshold-based risk detection, bridging machine learning interpretability with practical safety management. The proof-of-concept model developed demonstrates how inflection-point analytics can support early warning systems, personalised interventions and data-driven policy design in safety-critical operational settings.

Construction project delays remain a persistent issue, often exacerbated by variation orders that adversely affect both financial and environmental performance, even in the UK. Although Modern Methods of Construction (MMC) have been increasingly employed to mitigate delays and improve efficiency, limited research has examined how variations affect small- and medium-sized enterprises (SMEs) adopting MMC in the UK construction sector. Given the pivotal role of SMEs and their financial vulnerability, this study examines the key challenges posed by variation orders for SMEs adopting MMC, with the broader aim of enhancing future project performance. Employing a two-stage iterative methodology, the research first identifies challenges through a comprehensive literature review, followed by a questionnaire survey and expert interviews. The resulting data were analysed thematically and statistically using SPSS and subsequently validated through a detailed case study involving interviews and document analysis. The findings highlight three principal clusters of challenges: operational, contractual, and module alteration-related, of which operational issues, particularly cost discrepancies, client approval delays, and rework, exert the most significant influence. The study provides a structured understanding of these interlinked challenges and underscores the need for targeted mitigation strategies to improve productivity and performance among UK construction SMEs engaged in MMC projects.

Utility strikes during excavation works remain a critical safety, financial and operational challenge in UK highway projects despite a mature regulatory framework and decades of prevention initiatives. This study systematically identifies and prioritises the multifactorial causes of such safety incidents using a hybrid qualitative-quantitative methodology combining Delphi-derived expert consensus with the Stepwise Weight Assessment Ratio Analysis (SWARA) technique. Data were collected from 36 senior UK highway project managers and engineers, yielding a robust ranking of 33 sub-criteria grouped under two primary dimensions: workrelated (weight = 0.589) and worker-related (weight = 0.386) factors. Key findings reveal that the dominant causes are: failure to adhere to safe systems of work (SSoW) (rank 1, weight contribution 0.17); operators or workers unfamiliar with site layout and site-specific controls (rank 2); lack of availability, incorrect or overly complex buried utility drawings and accompanying information (rank 3); a worker ignoring the training provided (information, instruction and training (rank 4); and safety versus production goal conflicts (rank 5). These results highlight a systemic breakdown in procedural execution, data quality and incentive alignment. Workerlevel issues, particularly unfamiliarity with site-specific controls and disregard for training, ranked second in overall importance. Emergent findings underscore the persistent implementation gap between safety policy and practice in UK highway delivery and highlight the urgent need for integrated socio-technical reforms. Practical recommendations include: mandatory 3D uncertainty-aware utility modelling; proficiency-based simulation training; contractual realignment of safety and production incentives; and establishment of a real-time national underground asset register. This research provides the first nationally focused, empirically weighted causation hierarchy for utility strikes in UK highway projects, offering a foundational evidence base for transformative safety risk reduction.

Purpose Reportedly, there has been a plethora of research that magnifies the benefits of integrating green roofs into building projects. However, its implementation is seen less in buildings in developing countries, including Ghana. Therefore, this study aims to analyse the barriers to the integration of green roofing systems in Ghanaian building projects. Design/methodology/approach The study adopts a quantitative methodological approach to address the research aim. First, a literature review is conducted to identify 15 barriers to green roof implementation. Next, the barriers are evaluated using fuzzy analysis techniques to group them and determine their criticality. Findings Findings indicate that the critical barriers to green roofs implementation can be categorised into three groups: technical-economic implementation barriers, policies and unsound assessments, and finally, support from government and damages to the environment. All the groups were identified as critical, with the most severe barrier factor being a low level of awareness. Research limitations/implications This study provides valuable insights for professionals and stakeholders in Ghana's building systems and environment to address the barriers to integrating green roofing systems. Strategies such as public awareness, financial incentives and establishing regulatory policies can alleviate key challenges. Originality/value As one of the pioneering studies on green roof barriers in Ghana, these findings establish a baseline for future research, offering a theoretical foundation for further exploration into sustainable roofing practices in the Ghanaian construction industry.

Alongside implementing circular economic principles, the concept of reverse logistics supply chains (RLSCs) of demolition waste (DW) has captured the construction industry's attention. Due to the escalating risk vulnerability, risk management (RM) in RLSCs of DW has emerged as a critical requirement yet, has not been reviewed extensively. To address this knowledge gap, this study synthesises extant literature to develop a comprehensive RM framework for RLSCs of DW. This was accomplished through two objectives viz.: i) to explore the existing level of scientific development in RM for RLSCs of DW; and ii) to study how risks are managed in RLSCs of DW with the aim of identifying key risks, risk assessment procedures, existing mitigation strategies, and potential gaps that need to be addressed for resilient and efficient DW management practices. In total, 35 pertinent articles sourced from two search engines published between 2000 and 2024 were subjected to descriptive and content analysis. Most articles reviewed collected data through field measurements/sample testing and originated from developed countries. The waste reprocessing stage is the most vulnerable to various risks, including health and safety, environmental, industrial, social and regulatory risks. Effective risk mitigation strategies proposed include establishing favourable policies and incentives, enhancing awareness, improving information sharing, incorporating innovative technologies, ensuring multi-stakeholder engagement and adopting safety measures. The study significantly contributes to RM in RLSCs of DW by providing an overarching foundation via the proposed conceptual framework, which guides organisations to articulate an appropriate RM strategy.

Purpose This research aims to identify the prevailing provision of building surveying educational courses within the United Kingdom (UK) and examine the pressures confronting the higher education (HE) sector via a critical analysis of published data. Such work has not been previously undertaken. An in-depth discussion is presented with an accompanying theoretical model of institutional decision-making which highlights the deleterious impacts of utilising prevailing statistical indicators to measure the sector’s performance. Design/methodology/approach An inductive and interpretivist methodological approach was adopted that utilises a three-stage “waterfall” approach to analyse secondary data. A sample of HE institutions offering building surveying courses is then critically analysed in terms of: course provisions and geographical location; prevailing accreditation and isolation from alternative courses within the wider built environment; and course performance and student satisfaction. Findings While statistical performance criteria used to measure building surveying courses’ performance meets UK Government-imposed checks and balances it systematically fails to align with societal needs to adequately repair, manage and improve building stock. The polemic discourse presented concludes that established wisdom regarding course performance measurement requires reconsideration to enable the HE sector to meet society’s future building and infrastructure demands. Originality/value A novel approach to analysing building surveying course provisions is provided that utilises the tools, techniques and methods adopted by governments and HE institutions themselves to measure course performance. The findings presented constitute the first attempt to critically analyse how the current modus operandi is impacting upon educational provisions.

Despite the success of machine learning (ML) in safety risk prediction across various industries, there is scant evidence to prove that variables used are reliable indicators of safety performance. This study provides a conceptual framework for identifying safety indicators and formulating variables from these indicators that will optimise the quality of data used for risk modelling for Highways Traffic Officers (HTOs). This aligns with Sustainable Development Goal (SDG) 3: Good Health and Well-being, particularly target 3.6, which aims to halve global road traffic deaths and injuries. A mixed philosophical stance was adopted to understand and interpret the literature on safety indicators (SI) from myriad perspectives. A three-phase iterative waterfall approach was adopted which includes: i) PRISMA-based bibliometric search to identify relevant literature; ii) scientometric and cluster analysis to identify significant SIs and key considerations for selecting appropriate indicators for different purposes; iii) grounded theory analysis to synthesise scientific discoveries. Literature on leading and lagging SIs identified pertinent considerations that must be made when selecting SIs for risk modelling (e.g., action and utility). Furthermore, leading and lagging indicators were combined to form resilient indicators that incorporate adaptability into the safety system-thereby increasing safety performance. This paper presents a novel conceptual framework for HTOs which inculcates resilient measures in SI selection for adaptability and recovery purposes; and a robust method of integrating leading and lagging indicators to create resilience. Cumulatively, the research presents the first study to provide detailed guidance on the specific criteria of ML variables significant for risk modelling and prediction.

Objective To understand in people with stroke: (1) reasons for cardiopulmonary treadmill exercise test termination, (2) how frequently secondary criteria indicating a maximal test are met, and (3) how test termination is related to volume of oxygen consumption and participant characteristics. Design A secondary analysis from the Promoting Recovery Optimization of Walking Activity in Stroke (NCT02835313) clinical trial. Setting Four outpatient rehabilitation clinics. Participants People with chronic stroke able to walk without assistance of another person. Intervention Participants ( n = 250) randomized in a larger clinical trial completed symptom limited graded exercise treadmill tests pre- ( n = 247) and post-intervention ( n = 185). Treadmill exercise tests were conducted at constant speed with incremental incline increases. Main Measures The primary measure was reason for cardiopulmonary exercise test termination. Secondary measures included: oxygen consumption, ventilatory threshold, peak heart rate, respiratory exchange ratio, six-minute walk test, and fastest walking speed. Results There were six categories of test termination, electrocardiogram (11%), blood pressure/heart rate (13%), biomechanical (25%), self-selected (41%), equipment malfunction (8%), and other (2%). Only 1.9% of tests achieved the threshold to confirm a maximal aerobic effort. There were no differences in peak volume of oxygen consumption or participant characteristics between test termination categories. Conclusions Analyses indicate few with chronic stroke exhibit a maximal aerobic effort on a cardiopulmonary exercise test. If the cardiorespiratory system is not thoroughly taxed during treadmill exercise tests in people with chronic stroke, interpreting results as their cardiorespiratory fitness should be done cautiously.

Green intellectual capital (GIC) is required to effectively improve environmental performance (EP) of construction organisations. However, the lack of an appropriate GIC model impedes environmental sustainability efforts. To bridge the existing practice gap, this study employed a partial least squares structural equation modelling method to develop a GIC model for the construction industry. The model confirmed a positive significant impact of green structural capital (GSC) (beta = 0.260, p < 0.001), green human capital (GHC) (beta = 0.283, p < 0.000) and green relational capital (GRC) (beta = 0.372, p < 0.000) on EP. GSC has a strong mediating role in the relationship between GHC and EP (beta = 0.795, p < 0.000) and a moderate mediating role in the relationship between GRC and EP (beta = 0.399, p < 0.000). Novel insights presented revealed the complexities in organisational dynamics that are key to achieving a sustainable built environment.

As the foundation of national development, the construction industry is one of the most hazardous industries in the world, facing safety challenges and high rates of work-related accidents, especially in developing countries such as Iran, where 35% of all industrial accidents are related to construction accidents. In the meantime, construction site layout (CSL) design is vital in improving safety and cost efficiency, but the lack of comprehensive frameworks has limited its effective application. Traditional methods also create inefficiencies and additional costs due to the lack of flexibility in the face of project-specific constraints and unpredictable conditions. Significant research gaps exist, especially in Iran, where socioeconomic and cultural factors affect construction methods. This study aims to identify and analyze the critical factors affecting CSL in developing countries and provides a comprehensive framework that integrates regional constraints with global best practices. The main criteria identified in order of priority are hiring skilled professionals (weight: 0.32), hazardous materials management (weight: 0.25), and using advanced technologies (weight: 0.18). We first conducted a Delphi survey with domain experts using a hybrid approach to identify and refine key factors. Next, we utilized the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and fuzzy logic to examine causal relationships among the factors. Additionally, we prioritized the factors based on their relative importance using the fuzzy analytic network process (FANP). This research provides a practical framework for CSL optimization that helps improve safety and reduce costs in construction projects.

Purpose Risk-based inspection (RBI) is a systematic method of inspection management in various industries, particularly oil and gas and related industries. This method primarily seeks to reduce financial costs, increase safety, determine inspection intervals and achieve maximum productivity. RBI is a potent risk analysis tool that uses a variety of qualitative and quantitative analyses. Given the novelty of this method, it is possible to upgrade and improve it using other available methods. Design/methodology/approach Intuitionistic fuzzy risk-based inspection (IFRBI) is a mixed method of RBI and intuitionistic fuzzy sets that performs well in dealing with many ambiguities of verbal expressions and risk analysis, along with solving ambiguous problems and cases where there is insufficient information. The current study aims to provide an IFRBI method in the oil and gas industry, which is highly sensitive and vital. Hence, the proposed method was first explained and then used to check and inspect the pressure vessel in a gas refinery in Iran. Findings The risk analysis and ranking results are used in the RBI program, indicating a high risk of personal injuries and financial losses and a moderate risk of environmental damage. It was also shown that the numerical analyses were understandable in the steps of these calculations, leading to more accurate results. Originality/value Managers and decision-makers in the oil and gas industry and researchers in various industries, including construction, can use the results of this study.

Despite the plethora of digital and technological advances made in the construction industry over the past three decades, at its core, the sector remains human-centric. Consequently, this research investigates the core soft skills employed on public linear infrastructure (PLI) projects (during the construction phase) that are digitally enabled and concludes with the development of a decision support tool for PLI project team management. A mixed philosophical stance is implemented using interpretivism, postpositivism and grounded theory together with abductive reasoning to examine subject matter experts’ perceptions of the phenomena under investigation. Textual analysis is then utilised to formulate a decision support tool as a theoretical construct. The research findings demonstrate that communication, leadership and creativity/curiosity are the three main soft skills required of PLI projects. Furthermore, the key elements of a decision support tool—namely, trackable and measurable data, clear objectives and success criteria, and an easy-to-understand visual format—were identified. Such knowledge provides a strong base for building an emotionally intelligent project team. This research constitutes the first attempt to understand the essential soft skills required on PLI projects and, premised upon this, generate a decision support tool for project management in teams that helps to augment project performance through workforce investment via a learning organisation.

Construction projects in developing countries indicate many implementation problems, such as the technical incompatibility of the implemented structure with the design, incorrect management, the prolongation of a very high percentage of projects up to several times of the planned period, and the increase in costs; it is vital for construction firms to gather, integrate, and communicate the results of project management procedures using tools and methods, including information systems, in order to reduce these problems. Evaluating the results of project management procedures, using tools and methods such as information systems, can be helpful to avoid implementation problems, technical incompatibility of the constructed structure with the design, improper management, delays, and cost overruns. Hence, this study aims to evaluate the influence of information systems on project management success through the mediator variable of project risk management in construction firms. To accomplish this, 95 Iraqi building specialists were picked as a statistical sample using snowball sampling. Three questionnaires were used as data collection tools including an information systems questionnaire with four dimensions and 27 questions, a project management success questionnaire with 27 questions, and a project risk management questionnaire with six dimensions and 25 questions based on a five-point Likert scale measurement. The validity and reliability of the questionnaires were checked and confirmed. Smart PLS 4 and SPSS 28 softwares were used for analyzing the data. Finally, the findings indicated that the impact effect as well as the full effect of information system variables on project management success without the presence of a mediator is significant. Moreover, the indirect effect of information system variables on project management success with the presence of a mediator is also significant. In addition, project risk management has a partial mediator effect on the effect of information system variables on project management success. Also, there is a considerable correlation between the use of information systems and the success of project and risk management. Moreover, in the first phase of stepwise regression, capacity development predicts project management success and risk management variables. The regression analysis revealed that among the dimensions of information systems, the Capacity Development dimension has the ability to predict the success of project management and project risk management.

PurposeThe purpose of this paper is to present a quantified model for influential factors in the collaboration process in Building Information Modelling (BIM). BIM-based Construction Networks (BbCNs), which comprise teams of specialist organisations engaged to execute BIM-related activities, have become the centrepiece of collaboration in construction projects. In BbCNs, however, a lack of effective collaboration among teams remains a major barrier to receiving the full benefits of BIM. Despite this importance, the role of influential factors in collaboration in BbCNs has remained somewhat esoteric in nature in various previous studies, in which the present study attempts to address this gap.Design/methodology/approachTo develop the quantified model for collaboration in BbCNs, primary empirical data was collected from a questionnaire survey of BIM experts in the construction industry. This data was subsequently analysed through the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique using SmartPLS software as a viable and robust package for PLS-SEM analysis.FindingsQuantification of associations to collaboration in BbCNs reveals that unlike the common beliefs espoused in previous studies on collaboration in BbCNs, the lack of BIM-related tools and technologies is no longer the key concern of effective collaboration in BbCNs. Instead, ethical and managerial factors including “ethical approaches”, “liabilities” and “BIM manager role” were found to be in need of more attention for effective collaboration in BbCNs.Practical implicationsThrough presenting the first quantified model for collaboration in BbCNs, findings provide a point of reference for practitioners for coaching and managing teams. So too, the findings can be translated into a set of guiding principles for the world of practice for enhancing collaboration in BbCNs.Originality/valueThis paper makes a significant contribution to the field by quantifying the factors that impact collaboration within BbCNs settings. It meticulously assesses the degree of influence wielded by these factors and provides empirical numerical evidence to demonstrate that the lack of BIM tools and software is comparatively less concerning in fostering collaboration within BbCNs when contrasted with human-related factors. This original contribution surpasses prior qualitative evaluations by introducing a systematic framework for ranking and comparing other influential factors, thereby advancing relevant theoretical constructs into the realm of quantitative analysis.

PurposeGiven the expansion of cities and urbanization, developing efficient and reliable transportation infrastructure, especially urban tunnels, is essential. Failure to maintain such complex construction facilities with intelligent equipment systems could result in human losses and impose huge costs on governments. Therefore, it is necessary to have practical maintenance plans and operational safety monitoring for urban tunnels, which leads to their long lifespan, increases users’ safety and reduces operation risks.Design/methodology/approachHence, this research aims to evaluate the maintenance risks of urban tunnel lighting systems (UTLS) using a hybrid risk-based maintenance (RBM) approach. In this vein, three rounds of a fuzzy Delphi survey were conducted to consolidate the specific operation criteria and maintenance risk factors to the circumstances of Iran and UTLS. Furthermore, the fuzzy DEMATEL method was applied to determine the cause-and-effect relationships among the identified critical operation criteria. The identified risks associated with maintenance in UTLS were then analyzed and ranked using a combination of fuzzy ANP-VIKOR techniques.FindingsThe ranking of the various risks revealed that the “poor performance of switchboards in power supply due to faults in switchboard equipment” risk was ranked first, followed by the “poor performance of panels in the power supply due to unfavorable environmental conditions,” “The poor performance of panels in the power supply due to problems with switches (key failure)” and “The poor performance of panels in power supply due to burning fuses due to unauthorized current” risks. The findings of this study indicate that this hybrid maintenance method, developed as a risk-based network, provides reliability for maintaining urban tunnel lighting systems (UTLS).Originality/valueIt is anticipated that the findings of this research will considerably contribute to improving UTLS maintenance management while enhancing different stakeholders’ understanding of the most critical risks in maintenance, particularly toward the UTLS in Iran. An RBM management program can result in preparing and formulating policies, comprehensive guidelines or regulations for the maintenance of urban tunnels that are recommended for future research.