Saeed Reza Mohandes

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.

Purpose: This study aims to systematically identify, categorize and prioritize barriers to the adoption of artificial intelligence (AI) in circularity-driven smart cities. By framing the analysis through the lens of the circular economy (CE), the research addresses the knowledge gap in understanding the technological, governance, socio-technical and infrastructural barriers that hinder AI integration in sustainable urban contexts. Design/methodology/approach: A picture fuzzy Z-analytic hierarchy process (PF Z-AHP) approach is employed to capture expert judgments under uncertainty and prioritize the identified barriers. The methodology integrates picture fuzzy sets with the analytic hierarchy process within a multi-expert decision-making framework to systematically evaluate and rank barriers related to digital infra-structure, governance and policy, socio-technical engagement and urban technological capacity. Findings: The results show that socio-technical and community engagement barriers hold the highest overall category weight, followed by circularity-enabling digital and data infrastructure barriers. At the individual level, skills gap and resistance to change emerges as the most critical barrier, highlighting the importance of human capital readiness in facilitating artificial intelligence–enabled circular transitions in smart cities. Originality/value: This research contributes to the literature by integrating fuzzy-based multi-criteria decision-making to examine AI adoption in circular economy-oriented smart cities. The study presents a novel application of PF Z-AHP for evaluating barriers under uncertainty, offering a replicable methodological framework for advancing sustainable, AI-driven circular urban transformations.

Purpose: This study aims to address the issue of insufficient exploration regarding how government policies impact the adoption of blockchain technology (BT) within the construction sector. To fill this gap, the research aims to develop a comprehensive framework using evolutionary game theory (EGT) to analyze the effects of BT policies. Design/methodology/approach: The methodology thoroughly examines BT policies, evaluating positive incentives and mandatory regulations, including subsidies and regulations, using EGT. This approach provides insights into how government policies influence BT adoption among construction stakeholders. Findings: Findings reveal stable evolutionary strategies among stakeholders and observe that government agencies' limited promotion results in low adoption rates. Government subsidies are suggested while also recognizing the limited effectiveness of punitive measures. This study highlights the importance of proactive government involvement in promoting BT adoption and stresses the significance of subsidies in facilitating this process. Originality/value: The implications of this research are relevant to policymakers and industry stakeholders, guiding the crafting of effective policies to drive BT adoption within the construction sector. The necessity of incorporating bounded rationality assumptions in mathematical modeling to capture decision-making dynamics accurately is emphasized.

Sustainability has become a critical focus in the construction industry, yet a detailed understanding of the factors shaping green building adoption remains limited. This study investigates the Strengths, Weaknesses, Opportunities, and Threats (SWOT) associated with green building implementation and employs a fuzzy-based DEMATEL approach to analyse the interrelationships among these factors. Key findings reveal that improved resident comfort, enhanced GDP, lack of end-user knowledge and awareness, and bureaucratic inefficiencies significantly influence green building adoption. Expert validation further identified that tax rebates, financial incentives, and subsidies are essential strategies for promoting sustainable construction practices. The study also highlights the importance of integrating regulatory support, stakeholder engagement, and sustainable technologies to enhance policy effectiveness. By systematically combining SWOT with fuzzy DEMATEL, the research provides a rigorous framework to assess factor importance and causal interdependencies under uncertainty. The outcomes offer actionable insights for policymakers and industry practitioners, guiding the design of incentive programs, regulatory frameworks, and implementation strategies. This study contributes to academic scholarship and equips decision-makers with a practical, evidence-based approach to advancing green building adoption, fostering sustainability, and supporting long-term development in the construction sector.

Purpose: Despite growing interest in digital transformation, the construction industry continues to face challenges in the consistent adoption of cloud-based construction management platforms (CCMPs). Existing literature often overlooks the combined influence of individual behavioural perceptions and organizational–environmental readiness. This study addresses this gap by integrating the technology acceptance model (TAM) and the technology-organization-environment (TOE) framework to develop a holistic model of CCMP adoption. Design/methodology/approach: Partial least squares structural equation modelling (PLS-SEM) was applied to data collected from 256 construction professionals to examine the factors shaping CCMP adoption readiness. Findings: The findings indicate that staff digital competence, cybersecurity confidence and platform interoperability significantly influence implementation readiness, mediated by perceived decision-making support and user learning curve. Organizational size was not found to moderate these effects. This study is original in developing and empirically validating an integrated TAM–TOE framework specifically tailored for CCMP, an area that has received limited theoretical and empirical attention. By bridging behavioural and contextual perspectives, the research provides a novel multi-level understanding of digital readiness in construction, offering actionable guidance for firms, platform developers, and policymakers to accelerate digital transformation. Originality/value: This study contributes an integrated TAM–TOE framework that captures both individual and organizational–environmental perspectives, offering actionable insights to guide strategic CCMP implementation in digitally evolving construction environments.

This study addresses the critical challenge of sewer pipeline failures, a problem of significant environmental and economic consequence. Despite the widespread impact, a research gap exists in quantitatively understanding the factors contributing to these failures, hindering the development of targeted mitigation strategies. The study introduces a novel amalgamation of Picture Fuzzy Set Theory with the Delphi technique (PFDM) and the System Dynamic Modeling (SDM) technique to fill this gap. Findings from content analysis reveal four main factors and twenty-three sub-factors, with environmental and structural elements emerging as predominant contributors to sewer pipeline failures. The proposed PFDM identifies eleven sub-factors as primary culprits, highlighting the critical role of factors such as pipe age, materials, damages from third parties, internal corrosion, and various types of cracks and holes. Importantly, sensitivity analysis demonstrates the robustness of these findings, showcasing consistency across diverse expert opinions. The SDM technique further underscores the interconnectedness of influential factors, emphasizing the need for targeted interventions. This research offers valuable insights for environmental and drainage decision-makers, helping them prioritize interventions to reduce sewer system failures and their associated environmental impacts, such as sewer overflow and exfiltration. By focusing on the identified critical factors and their complex relationships, the overall resilience and longevity of sewer pipelines will be enhanced.

Purpose: The purpose of this study is to propose a hybrid conceptual framework that systematically identifies, prioritizes and maps critical enablers of LC adoption in megaprojects, with a particular emphasis on their mutual dependencies and structural relationships. Design/methodology/approach: To achieve the research objectives, a mixed-method approach was employed. First, a systematic literature review was conducted to identify potential enablers of LC adoption. Second, semi-structured interviews were carried out with industry experts to validate and enrich the initial list of enablers. Finally, a fuzzy-based multi-criteria decision-making algorithm was applied to prioritize and map the enablers based on their influence and dependency, enabling a deeper understanding of their structural relationships. Findings: The analysis revealed six main enablers and thirty associated sub-enablers critical to LC adoption in megaprojects. Among these, “Strategic and Leadership” and “Stakeholder and Relationship Management” emerged as the most influential enablers. Key sub-enablers identified include government and top management support, early stakeholder engagement, resource readiness and lean proficiency, lean tools implementation, standardized performance metrics and BIM and technology adoption. These findings provide insight into the core drivers and their interdependencies, offering actionable intelligence for effective LC implementation. Originality/value: This study contributes to both academic literature and industry practice in two key ways. First, unlike previous studies that merely list enablers without exploring their interconnections, this research provides a detailed mapping of enabler dependencies and interactions, offering a more nuanced understanding of LC adoption dynamics. Second, it introduces a novel hybrid framework combining systematic literature review, expert validation and fuzzy logic to prioritize and structure LC enablers.