Purpose This paper aims to propose a data interoperability framework involving digital twins (DT) for construction lifecycle carbon management. Technical and semantic constraints affecting the coordination across building information modelling (BIM), lifecycle assessment (LCA) and DT environments are examined. The architecture ensures information continuity across asset lifecycle. Design/methodology/approach Bibliometric mapping and a PRISMA-guided systematic review of 105 core studies outline barriers to carbon data coordination. A thematic synthesis supports an interoperability framework formulation aligned with ISO 19650 principles. Findings Identified barriers include non-standardised schemas, static emission factors and BIM-LCA interoperability gaps. The framework links DT attributes with carbon parameters via ontology-based semantic modelling. Such integration has the potential to mitigate information loss during Industry Foundation Classes transitions and support dynamic embodied carbon tracking. Research limitations/implications The system relies on literature synthesis without full-scale empirical validation. Future investigations should evaluate framework application and scalability in operational projects across diverse regulatory contexts. Practical implications Exchange points for lifecycle datasets facilitate specific workflows. Designers apply semantic mapping for material selection, contractors synchronise supply chain emissions and asset managers execute operational decarbonisation via the DT. Social implications Formalising semantic alignment improves emissions traceability and impact verification. Broader industry shifts towards net-zero policies and sustainable procurement practices represent indirect outcomes. Originality/value The research addresses embodied carbon interoperability, expanding beyond operational energy. An ISO 19650-aligned framework integrates BIM, LCA and DT environments. Formalised semantic interoperability requirements establish structured lifecycle carbon management.
Purpose This study aims to address the strategic gap in Building Information Modeling (BIM) implementation by developing and empirically validating a novel, process-based framework (BEP-V) that embeds Value Engineering (VE) directly into the BIM Execution Plan. Design/methodology/approach The framework was empirically validated through a real-world case study of a mixed-use building in Taiz, Yemen. A dual-phase VE integration was applied: first, a Quality Model was structurally embedded during the early architectural programming phase to guide conceptual design. Second, BIM (Autodesk Revit) was used for automated data extraction, while the analytic hierarchy process (AHP) was applied for multi-criteria evaluation of the generated alternatives. Findings The empirical application validates that shifting VE to a mandated BEP workflow significantly enhances design performance. Early intervention via the Quality Model prevented low-value designs from the outset, while the subsequent AHP evaluation facilitated a data-driven facade optimization. This synergistic approach achieved a substantial 73.0% reduction in execution costs while maintaining acceptable thermal performance (U-value: 2.37 W/m²K) and high architectural quality. Practical implications The framework provides AEC practitioners with a reproducible, value-driven digital roadmap. It proactively mitigates design errors, eliminates unnecessary costs and enhances multidisciplinary coordination from project inception through detailed design. Originality/value This study transcends traditional tool-based BIM-VE integration by conceptualizing a strategic, process-based merger. It offers a paradigm shift in construction management, establishing the foundational digital governance required for future AI-driven and automated value optimization environments.
Purpose This study aims to investigate the key risks and barriers associated with the adoption of artificial intelligence (AI) for risk management in modular construction in the Nigerian construction industry. It addresses the need for data-driven risk management while accounting for diffusion-related constraints affecting AI adoption in developing country contexts. Design/methodology/approach A quantitative research approach was adopted using a descriptive survey design. Data were collected through a structured questionnaire administered to 309 construction professionals in Nigeria. The Relative Importance Index (RII) was used to rank critical risks in modular construction. Exploratory factor analysis (EFA) was used to categorise barriers to AI adoption, while fuzzy synthetic evaluation (FSE) was applied to assess the relative criticality of the identified barrier clusters. Diffusion of Innovation (DOI) theory was conceptually operationalised through a DOI–barrier attribution matrix. Findings The results indicate that technological risks (RII = 0.91) represent the most critical challenge in modular construction, followed by financial (RII = 0.87), health-related (RII = 0.86), market and investment (RII = 0.84) and project schedule risks (RII = 0.83). EFA identified five barrier clusters explaining 72.55% of the variance. FSE results reveal that Human Resource and Value Concerns constitute the most critical barrier cluster (index = 4.366, coefficient = 0.203), followed by Integration and Collaboration (4.299), Technical and Structural (4.287), Policy Trust and Implementation (4.286) and Awareness and Legal Framework (4.231). The DOI–barrier attribution matrix confirms that complexity and compatibility-related barriers are the primary constraints on diffusion. Research limitations/implications This study emphasises the important role of AI-driven risk management in addressing key barriers to modular construction, especially in developing economies. The findings highlight how AI can mitigate financial, safety and technological risks, thus enhancing project coordination, worker safety and supply chain optimisation. Moreover, the study underscores the need for workforce development, particularly in AI training and inter-industry collaboration, to bridge the skills gap and facilitate successful AI integration. This study contributes to the literature by demonstrating that the successful adoption of AI in modular construction requires a holistic approach that integrates technical advancements, policy reforms and stakeholder education to overcome barriers effectively. The primary limitation of this study lies in its focus on Nigeria, which may limit the generalisability of the findings to other developing economies or technologically advanced countries. Given that AI adoption in modular construction may vary across regions with different levels of technological infrastructure, the barriers identified in this study may not be directly applicable to other contexts. Furthermore, as the construction industry and AI technologies evolve rapidly, future studies should examine how emerging AI tools may affect the challenges and opportunities identified in this research. Originality/value The DOI–barrier attribution matrix is the primary original contribution, the first to map empirically derived AI adoption barriers to DOI constructs in a modular framework. Combined with the multi-analytical RII-EFA-FSE framework, this study offers a replicable, diffusion-oriented tool to accelerate AI adoption in developing-country construction contexts.
Purpose This study aims to explore the organisational factors in carbon trading for the Australian construction industry and develop overall organisational factor index level using fuzzy-logic evaluation technique. Design/methodology/approach Detailed review of past literature led to identification of 18 factor list that was ranked by industry experts in Australia through a survey. Purposive sampling technique was adopted. Findings The factors were grouped using component analysis and they were the input variables for the fuzzy logic-based analysis. The four components were: Incentives and technology factors (ITFs), Transaction costs and project complexity factors (TCPC), Carbon quota and standardisation factors (CQS) and Technological innovation factors (TIF). The findings of this study indicated that overall organisational factor level is high implying the influence of organisational factors in carbon trading in construction is significant. Originality/value This study demonstrates how fuzzy logic methodology is useful as an evaluation technique in carbon trading assessment. Furthermore, the findings contribute towards the construction industry’s fight against climate change menace.
Purpose Primary healthcare buildings remain underrepresented in healthcare-energy research, and systematic monitoring of energy consumption in Basic Health Units is largely absent in many public systems. This study aims to address this gap by investigating the relationship between climatic conditions and electricity consumption in primary healthcare buildings and by proposing a replicable framework for infrastructure and energy management under climate variability. Design/methodology/approach A case study was conducted in Araranguá, Southern Brazil, combining a 27-month energy audit of nine Basic Health Units with surveys of facility managers. Pearson correlation and regression analyses were applied to assess the sensitivity of electricity consumption to temperature variation. Findings Minimum monthly temperature showed a strong positive correlation with electricity consumption (r = 0.716), explaining over 50% of demand variability. Three recurrent performance issues were identified: continuous operation of equipment outside clinical hours, absence of preventive maintenance and limited natural ventilation in legacy buildings. Research limitations/implications The findings are based on a single case involving nine units over a 27-month period in a subtropical coastal municipality, which limits direct generalisation. Future research should test the framework across different climatic and institutional contexts and assess the long-term effectiveness of ISO 50001-based interventions. Practical implications The proposed framework offers municipal managers and facility operators a low-cost pathway to integrate energy monitoring, preventive maintenance and climate-responsive practices into asset management. Based on diagnostic evidence and literature benchmarks, such measures may contribute to electricity savings (potentially up to 30%), although these estimates were not empirically tested within the scope of this study. Originality/value This study contributes to the healthcare-energy literature, predominantly focused on hospitals, by providing empirical evidence on the climate sensitivity and operational performance of primary healthcare buildings. It also proposes an ISO 50001-aligned framework suitable for application in medium-sized cities in the Global South and positions primary care facilities as strategic assets within urban sustainability and SDG 11 agendas.
Purpose Lean construction (LC) and building information modelling (BIM) are viewed as complementary approaches for improving operational efficiency and value delivery in construction projects. Nevertheless, their combined implementation within the Indian construction industry remains uneven and fragmented. This study aims to examine the major constraints limiting LC–BIM adoption and to develop effective strategies for overcoming these constraints within the Indian industry context. Design/methodology/approach A structured decision-support framework was adopted to address the research objectives. Thirty-four implementation barriers from earlier studies were classified into 6 categories and 11 mitigation strategies were proposed. Expert inputs were analysed using the fuzzy analytical hierarchy process to prioritize strategies, while the fuzzy technique for order of preference by similarity to ideal solution ranked barriers by impact. Survey data reliability was confirmed using Cronbach’s alpha. Findings Skill, organizational and financial barriers are the top three critical challenges for the implementation of Lean–BIM integration. To address these challenges, the most effective strategies include client-mandated contractual requirements for Lean–BIM adoption, strong top management support and hiring Lean–BIM experts to provide project-level training and guidance. Originality/value This study extends Lean–BIM adoption literature by demonstrating that implementation barriers in emerging construction economies are primarily socio-organisational rather than technological. Applying a socio-technical systems perspective, the study explains how organisational readiness, workforce capability and stakeholder coordination shape Lean–BIM implementation outcomes. The findings provide a theoretically grounded prioritisation framework to support managerial and policy-level decision-making for Lean–BIM adoption in developing construction contexts.
Purpose This study aims to systematically evaluate and compare existing neighborhood-level social sustainability assessment tools (SSATs) by examining their hierarchical indicator structure, coverage, level of convergence and representational balance to evaluate their ability to represent and measure the multidimensional construct of social sustainability. Design/methodology/approach A structured coverage analysis was conducted on 23 neighborhood-level SSATs, including 19 academically developed tools and four widely used formal rating systems (LEED for Neighborhood Development, BREEAM Communities, MOSTADAM Communities and LEED for Cities and Communities). Through an iterative content analysis process at the operational level of the indicator, a total of 605 indicator instances were extracted, of which 213 were unique indicators, grouped into six categories and 18 subcategories, forming the Comprehensive Social Sustainability Assessment Framework (CSSAF). Breadth and depth of coverage were examined against CSSAF at the category, subcategory and indicator levels. Convergence among tools was quantified using vector-based weighted Jaccard similarity. Findings Results reveal weak convergence across neighborhood-level SSATs. Approximately 83% of CSSAF indicators appear in only one or two tools, and no tool covers more than 78% of CSSAF subcategories. Similarity analysis further confirms substantial fragmentation among tools (mean J = 0.096; median J = 0.091), with many tool pairs demonstrating near-zero overlap. Coverage is also systematically imbalanced, with indicators concentrated on livability-related dimensions, particularly access to amenities/services and housing, while urban integration and governance-related dimensions, such as community resilience, external integration and civic engagement, remain consistently underrepresented. Formal rating systems provide more standardized and auditable assessment structures but generally capture a narrower portion of the social sustainability construct, as defined by CSSAF, compared with academically developed tools. Originality/value This study develops a traceable consolidated framework (CSSAF) and provides a comprehensive set of coverage metrics that can diagnose the comprehensiveness, representativeness, convergence and structural consistency of existing neighborhood-level (SSATs). The resulting framework supports more transparent tool selection, identification of systematic underrepresentation and coverage gaps, and development of more balanced and context-sensitive social sustainability assessment systems.
Purpose Despite global evidence of retrofitting effects on building performance, there is a key research gap: the impact of retrofitting policies on rental price appreciation in the student housing rental market. This study aims to examine the quantile effects of retrofitting on rental values across on-campus student housing retrofitting in Ghana. Design/methodology/approach The study adopted a quantitative research design, combining an experimental block design, a repeated sales model and simultaneous quantile regression. The study measured rental appreciation from the 10th to 90th quantile to determine the distributional effects across 212 on-campus student housing in Ghana. Findings The findings suggest that retrofitting does not have a significant impact on rental appreciation in the lower and median segments of the student housing rental market. However, retrofitting policies have a strong positive effect at the 90th percentile. This distributional asymmetry implies that, the benefits of retrofit are concentrated in higher on-campus accommodation while the lower and middle market segments remain largely unaffected. Practical implications Student accommodation investors should consider overall housing quality, service reliability and tenant satisfaction, as well as retrofitting, to enjoy guaranteed rental appreciation. This is more effective in higher-value student accommodation. Originality/value The application of quantile regression to examine the benefits of retrofitting in student accommodation reveals heterogeneous effects across on-campus student housing within the African context.
Purpose The purpose of this study is to identify and prioritize the sustainability indicators (SIs) perceived as most influential for project success within Iran’s volatile socioeconomic environment. It aims to bridge the gap between global sustainability standards and local operational realities in resource-constrained contexts. Design/methodology/approach A sequential mixed-methods design used a hybrid multicriteria decision-making framework. First, the fuzzy delphi method (FDM) screened indicators using an 11-expert panel. Second, the method based on the removal effects of criteria (MEREC) objectively weighted the success criteria based on data variance. Finally, the combined compromise solution (CoCoSo) method ranked the SIs. Findings The analysis reveals a context-specific prioritization where financial analysis, project safety and resilience emerged as the top-ranked indicators, while renewable materials ranked lowest. This suggests that in sanctioned, high-inflation settings, practitioners prioritize economic viability and operational risk mitigation; broader environmental aspirations are strategically deferred until foundational project continuation is secured. Research limitations/implications The findings reflect expert judgments predominantly from heavy industrial sectors in Iran. Therefore, results are context-bound and may not fully capture the nuances of agile sectors (e.g. software) or translate to stable macroeconomic environments. Practical implications For practitioners in volatile markets, the study provides a resource allocation roadmap. It recommends integrating financial and safety metrics into core control systems (e.g. earned value management) and dynamic risk registers, moving beyond “one-size-fits-all” sustainability checklists. Originality/value This paper integrates the FDM-MEREC-CoCoSo framework into sustainable project management to minimize subjective bias. It contributes empirical evidence extending contingency theory by demonstrating that macroeconomic volatility fundamentally reorders sustainability priorities, reframing sustainability in volatile environments as a pragmatic risk-management strategy.
Purpose This study aims to examine how value engineering (VE) can be aligned with the 3R principles – reduce, reuse and recycle – to advance sustainable construction practices. It empirically investigates the key drivers, challenges, benefits and enabling tools and techniques influencing the adoption of 3R-driven VE across different stages of the construction project lifecycle, thereby bridging the gap between conceptual frameworks and practical implementation. Design/methodology/approach A mixed-methods strategy was adopted, combining a systematic literature review, a structured questionnaire survey of 200 construction professionals from 14 countries and 30 semi-structured expert interviews. The Relative Importance Index (RII) was used to prioritize influencing factors, while thematic analysis of interview data was used to validate, enrich and contextualize the quantitative findings. Findings The findings reveal that supportive government policies, early integration of VE during project planning and structured functional analysis are the most influential drivers of 3R-driven VE adoption. Major challenges include limited awareness of economic and sustainability benefits, shortage of skilled professionals and lack of standardized tools for sustainable material management. Key benefits include reduced construction waste, improved alignment between lean construction and sustainability objectives and enhanced lifecycle performance. Building information modeling-based material tracking and life cycle assessment emerged as the most critical enabling tools. Practical implications The paper offers an environment for practitioners and policymakers to integrate 3R methods into VE, facilitated by digital technologies and incentive-based contracts, thus promoting cost-effective, waste-reducing and resilient building methodologies. Originality/value This study contributes to sustainable construction research by providing a comprehensive empirical assessment of VE implementation grounded in the 3R principles. It advances practical understanding of circular economy integration by synthesizing drivers, challenges, benefits and enabling tools and techniques within a unified, phase-based framework spanning the construction project lifecycle.
Purpose Social sustainability is a critical pillar of holistic sustainable development within the construction industry. However, its assessment in Ghana remains underdeveloped due to the absence of empirically validated, context-specific indicators. To address this gap, this study aims to examine the perspectives of stakeholders of key performance indicators for assessing the social sustainability of building construction projects in Ghana’s construction industry. Design/methodology/approach An explanatory sequential mixed-methods design was adopted. A literature review informed the development of a questionnaire capturing key social sustainability performance indicators (SSIs). Using purposive and snowball sampling, 167 responses were obtained from contractors, consultants, clients, academics and government professionals in Ghana’s building construction industry. Quantitative data were analysed using SPSS version 27 and descriptive and inferential statistics. To validate and enrich the three highest-ranked survey findings, six expert stakeholders were interviewed. Qualitative data were thematically analysed and triangulated with the survey results to confirm the relevance and contextual applicability of the identified indicators. Findings The results show that all nine indicators identified from the literature were considered important for assessing the social sustainability performance of construction projects. Among these, occupational health and safety, public health and safety, and impacts on existing infrastructure emerged as the top three priority indicators. Interview insights further reinforced the significance and practicality of these indicators within Ghanaian building projects. The study also revealed notable differences across stakeholder groups in how they ranked the indicators, highlighting the diverse expectations shaping social sustainability considerations in project delivery. Originality/value This study contributes to the limited body of research on social sustainability in developing-country contexts. The findings underscore critical areas that professionals and policymakers must address to strengthen social outcomes and advance the broader social sustainability agenda within Ghana’s construction industry.
Purpose Psychosocial safety climate (PSC) is essential for fostering sustainable development in the workplace. However, limited attention has been given to understanding how PSC is performed, particularly in high-risk industries like construction. Therefore, this study aims to examine the performance of different PSC dimensions in the Vietnamese construction industry. Design/methodology/approach A survey using the PSC-12 scale was administered via snowball sampling. Confirmatory factor analysis (CFA) was conducted to validate the PSC-12 scale’s constructs using data from 238 Vietnamese respondents. After that, the fuzzy synthetic evaluation (FSE) was used to evaluate the level of PSC performance. Findings The CFA confirmed the PSC-12 scale’s validity for measuring PSC among Vietnamese construction firms. The Welch ANOVA and Games–Howell test highlighted differences in PSC performance across construction firms of different sizes (i.e. small, medium and large). After that, the FSE analysis showed that large firms had the highest PSC performance, followed by medium and small firms. Moreover, the organizational communication factor has the highest performance score in small and medium firms. In contrast, organizational participation has the lowest performance value. Originality/value This study emphasized the crucial need to enhance organizational participation by fostering informal discussions and open-door policies in firms of all sizes. Medium and large-sized firms should establish formal consultation mechanisms, such as employee surveys and committees. Strengthening management commitment and prioritization is also crucial, requiring visible actions, resource allocation and integration of PSC into organizational strategic objectives.
Purpose Schedule delay remains a persistent challenge in large-scale infrastructure programmes, yet many predictive studies prioritise model accuracy while offering limited support for managerial interpretation and decision-making. This study aims to develop and evaluate a case-grounded, explainable hybrid machine-learning framework for schedule-delay risk prediction and decision-support translation. Design/methodology/approach The study integrates random forest and support vector machine within a weighted ensemble, with genetic algorithm optimisation applied to the random forest configuration. Shapley additive explanations is used to interpret model-attributed predictive contributions and identify non-linear risk-state patterns. The framework uses 470 case-grounded, programme-level risk-state observations derived from screened expert assessments and cross-checked against project documentation, practitioner interviews and schedule records. Findings The random forest-genetic algorithm + support vector machine model produced strong predictive results within the studied data set, achieving 92.2% accuracy with balanced precision, recall and F1-score values. The explainability analysis indicates that predicted schedule-delay risk is associated with non-linear, model-attributed risk-state patterns rather than isolated variables. Material and equipment supply, contractor performance and design changes emerged as the most salient predictive signals. These signals represent model-attributed contributions to predicted delay probability, not causal effects. Research limitations/implications The study is based on a single large-scale infrastructure programme; therefore, broader transferability requires validation across multiple programmes, delivery systems and national contexts. The data are partly expert-informed, although screened and cross-checked against documentary and schedule evidence. SHAP-based explanations are model-attributed and should not be interpreted as causal effects. Future research should examine temporal modelling and benchmarking against XGBoost, LightGBM, CatBoost and deep-learning approaches. Practical implications The framework supports structured managerial prioritisation by translating model outputs into operational risk bands, trigger conditions, responsible actors, intervention timing and measurable outcomes. Social implications By translating delay-risk predictions into explainable and trigger-based decision-support actions, the framework can support more transparent and accountable infrastructure project governance. Earlier identification of schedule-risk conditions may help reduce avoidable delays, resource waste, contractual disputes and disruption to public-service delivery. In large infrastructure projects, improved schedule-risk management can contribute to more reliable delivery of assets that affect communities, economic activity and public-sector investment efficiency. However, the framework should support, not replace, professional judgement and stakeholder accountability. Originality/value The study contributes by integrating hybrid prediction, explainable artificial intelligence and operational decision-support translation within a case-grounded infrastructure programme context. Rather than treating predictive accuracy or SHAP rankings as final outputs, the framework links predicted delay probability, dominant model-attributed risk signals and operational domains to trigger-based decision support.
PurposeDespite extensive research on Internet of Things (IoT) applications in the construction industry, there remains a critical gap in understanding its systematic implementation and effectiveness, specifically within construction supply chain management (CSCM). This study aims to provide a systematic review of IoT applications in CSCM, highlighting their current applications, associated challenges and emerging integration pathways. Design/methodology/approachA PRISMA-based systematic literature review was conducted, drawing on 28 peer-reviewed journal articles and conference papers from Scopus and Web of Science for bibliometric and qualitative content analysis. FindingsThe review shows that IoT has moved from isolated tracking solutions (e.g. Radio Frequency Identification-based logistics) to integrated ecosystems that combine Building Information Modelling, blockchain and digital twins to enhance visibility, traceability and coordination. Applications span areas such as real-time tracking, logistics optimisation, risk management, sustainability reporting and automated financial trust mechanisms (e.g. IoT-enabled smart contracts). However, widespread adoption is constrained by technical limitations, data governance issues, trust and cybersecurity concerns, organisational inertia and uncertain return on investment (ROI). The findings of this study highlight that IoT’s transformative value lies in its integration with complementary digital technologies, creating interoperable, governance-driven systems that improve collaboration, resilience and sustainability. Originality/valueThis study consolidates fragmented knowledge on IoT in CSCM, offering a structured synthesis of the construction supply chain’s current landscape. This review contributes a foundational roadmap for researchers and practical guidance for industry stakeholders seeking to leverage IoT for enhanced transparency, efficiency and sustainability in construction supply chains.
PurposePsychological distress is a growing concern in the construction industry, yet its relationship with work performance remains poorly investigated. Most existing studies assume a linear and negative association, overlooking the potential complexity of this relationship. This study aims to investigate the relationship between psychological distress and work performance, with a focus on the Vietnamese construction context.Design/methodology/approachA total of 422 valid responses were collected using a snowball sampling approach. Several machine learning (ML) algorithms were applied to explore the relationship between psychological distress and work performance.FindingsGaussian Process Regression was the most robust approach (RMSE = 0.3892, MAE = 0.2842, MASE = 1.2172, R2 = 0.8481). The analysis revealed an inverted U-shaped relationship consistent with the Yerkes-Dodson law. Specifically, work performance improved under mild psychological distress but declined beyond a threshold of 8.75.Originality/valueThe results underscore the importance of understanding and managing distress to remain productive. The study highlights that work performance improves with mild distress, but begins to decline sharply when psychological distress passes a certain level. These findings suggest that employees should not try to eliminate all distress, but instead focus on keeping distress at a healthy level.
Purpose Green building information modeling (Green BIM) offers significant potential for advancing sustainable construction practices. However, implementing Green BIM requires construction firms to align their strategies, processes and workforce capabilities toward sustainability goals. Despite its importance, limited research has examined how ready construction firms are to adopt Green BIM. Therefore, this study aims to assess the readiness of construction firms in Vietnam to implement Green BIM. Design/methodology/approach The MIT90s framework was applied to assess five internal organizational dimensions: strategy, structure, management processes, individuals and roles and technology. A total of 241 valid responses were collected using purposive sampling. Data were analyzed using the fuzzy synthetic evaluation method to determine readiness levels across firms of different sizes. Findings The findings highlighted that firm size plays a significant role in shaping readiness. Large firms demonstrated the highest readiness, followed by medium-sized firms. Small firms showed the lowest levels across all five dimensions. The results indicated that larger firms have clearer strategies, better-defined structures and stronger technical infrastructure. In contrast, small firms faced resource, planning and workforce-capability constraints. Originality/value This study highlights that Green BIM adoption requires not only technological tools but also comprehensive organizational alignment. The research contributes to theory by validating the use of the MIT90s framework in sustainability-driven innovation. Practical implications are offered for firms, policymakers and project stakeholders to support improvements in readiness.
Purpose - This study aims to examine the role of governance, integration and policy-oriented social strategies in shaping CE-based infrastructure projects in Malaysia. By identifying, categorizing and validating key social strategies, this study provides empirical support for theoretically derived dimensions, highlighting the essential role of social-organizational in CE implementation. Design/methodology/approach - This study engaged 156 practitioners and experts who are directly involved in CE-related initiatives from various regions throughout Malaysia. Exploratory factor analysis established the core social strategic dimensions, followed by partial least squares - structural equation modelling to examine the hypothesized relationships. Findings - The findings highlight that social sustainability in CE-based infrastructure is not driven by isolated interventions, but by the systemic alignment of transparent governance, inclusive integration and coherent policy frameworks. These three strategic dimensions were found to significantly and positively influence social sustainability in CE-based infrastructure projects (p < 0.01). Governance enhances accountability, transparency and trust; integration facilitates participatory collaboration among public, private and community actors; and policy institutionalizes inclusivity and long-term societal benefits. Originality/value - This study advances CE scholarship by positioning social strategies as a core pillar, rather than a peripheral element, of circular transformation. It provides empirical validation for a holistic framework that embeds governance, stakeholder inclusion and policy coherence into CE-based infrastructure planning. The findings offer practical guidance for policymakers and industry leaders involved in ongoing national initiatives such as the Twelfth Malaysia Plan and the National Circular Economy Roadmap (2021-2040), and contribute to broader international sustainability agendas, particularly the United Nations Sustainable Development Goals.
Purpose This study aims to address the limitations of lagging indicators and static risk assessments by developing an automated framework to model the dynamic nature of construction accident causation. The objective is to transition safety management from reactive compliance to predictive risk mitigation by forecasting how hazards propagate across construction sites over time.Design/methodology/approach The framework uses construction incident reports from the Occupational Safety and Health Administration database. The methodology begins by applying natural language processing and semantic clustering to define a validated hazard taxonomy from unstructured narratives. Next, a temporal graph is constructed to model hazard co-occurrences within a 30-day metropolitan window. Finally, edge-aware Graph Neural Networks, including GraphSAGE, graph attention network, Graph Convolutional Network and Graph Isomorphism Network, are used to formulate hazard forecasting as a link prediction task.Findings The GraphSAGE architecture equipped with convolutional block attention module attention yielded the highest predictive performance (mean reciprocal rank = 0.537), outperforming baseline models. Temporal features were identified as the primary driver of predictability, outweighing static structural associations. The model successfully mapped high-probability hazard chains, such as the progression from demolition to structural and struck-by incidents.Practical implications The predictive framework enables safety managers, regulators and planners to anticipate imminent risks and deploy targeted interventions, optimizing resource allocation and site inspection schedules before sequential accidents occur.Originality/value This research provides an end-to-end framework that transforms unstructured text into a dynamic temporal graph. It offers a data-driven tool for forecasting regional hazard pathways, empirically validating theories that treat safety risk as a dynamic property of temporal alignment.
PurposeThe structure and parameters of artificial neural networks (ANNs) are typically selected based on experience. This study aims to present a dynamic optimization model using metaheuristic algorithms in combination with ANN to achieve more reliable labor productivity predictions in construction project activities. Design/methodology/approachThis study focuses on optimizing and selecting the best metaheuristic algorithm from a set of candidates in conjunction with ANN. The goal is to intelligently attain the optimal combined model for weights and biases by minimizing mean squared error and ensuring an appropriate convergence process. This approach facilitates more accurate predictions of the target variable. FindingsA case study was conducted to predict labor productivity in a construction project. Seven metaheuristic algorithms – Salp Swarm Algorithm, Particle Swarm Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, Bat Algorithm, Firefly Algorithm and Sine Cosine Algorithm – were used as candidates for integration with ANN to forecast workshop labor productivity. The findings of this study indicated that, in alignment with the case study data, WOA-based training performed better than other candidates in the intelligent system, providing more accurate results in terms of both accuracy and convergence. Practical implicationsThis flexible hybrid dynamic model enhances the reliability of predictions related to activity data sets by automatically identifying the best optimizer. It can adapt to various workshop conditions and serves as a valuable tool for managers to understand and implement timely management strategies and techniques for improving productivity. Originality/valueThe proposed flexible hybrid intelligent model increases the accuracy of labor productivity estimates tailored to diverse activities in construction projects. It is repeatable and adaptable to various workshop conditions. Compared to other candidate combinations, the proposed model intelligently discovers the optimal algorithm, surpassing conventional methods in terms of accuracy, convergence, performance evaluation and error reduction.
