Behzad Abbasnejad

This research presents a novel neuro-fuzzy system dynamics (NN-Fuzzy-SD) model designed to manage risks in off-site construction supply chains (OCSCs). By combining the predictive power of neural networks (NNs) with the uncertainty-handling capabilities of fuzzy logic, the model enables dynamic, real-time risk assessment and mitigation, addressing challenges such as material shortages, transportation delays, and labor disruptions. The model integrates real-time data to optimize inventory buffers, resource allocation, and scheduling strategies, improving project efficiency and resilience. An empirical case study across six off-site construction projects in Beijing and Shanghai demonstrates the model’s effectiveness in adapting mitigation strategies according to varying risk profiles, yielding measurable improvements in cost efficiency and operational performance. This study makes four primary contributions to construction engineering and management. First, it introduces the first integrated NN-Fuzzy-SD framework for real-time risk management in off-site construction. Second, it develops the dynamic risk mitigation strategy optimization (DRMSO) and cross-domain neuro-fuzzy decision support system (C-DNF-DSS) for adaptive, cross-domain mitigation. Third, it empirically validates the model with real-world projects, providing quantitative evidence of improved resource allocation. Finally, it identifies dominant risk drivers and delivers a scalable, industry-ready framework that enhances project resilience, operational efficiency, and informed decision-making, contributing significantly to the global construction management body of knowledge.

The construction sector faces environmental pressures from material use, waste, energy and carbon emissions. Building Information Modelling (BIM) and Design for Manufacture and Assembly (DfMA) can support environmental improvement through more integrated design, production and assembly. However, environmental benefits are often attributed to BIM-enabled DfMA without direct assessment, and it remains unclear which outcomes have been demonstrated and which mechanisms are associated with them. This study addresses this gap through a systematic literature review of 43 studies published between 2018 and 2026. Using an evidence-based management lens, the review examined BIM–DfMA integration, the environmental outcomes and mechanisms reported, and their evidential basis. Material efficiency and waste reduction were the most frequently reported outcomes and had the largest directly assessed evidence base, whereas carbon reduction, energy efficiency and circularity were less frequently assessed. Design optimisation, reduced rework, standardisation and reduced component variation were the most frequently identified mechanisms. The review proposes a conceptual framework showing that BIM–DfMA adoption alone does not establish environmental improvement and that environmental performance needs to be considered in relation to environmental criteria embedded in decisions, the material and construction system, and the lifecycle stages assessed. BIM–DfMA workflows should therefore include measurable environmental criteria, defined baselines and appropriate assessment boundaries to support consistent evaluation of environmental claims.

Purpose: The integration of Building Information Modelling and Immersive Technologies (BIM–ImT) can enhance design, communication and stakeholder collaboration. However, its implementation remains difficult because technical capabilities and social conditions must be aligned. The study applies socio-technical systems theory as a classification lens to distinguish technical and non-technical criteria influencing BIM–ImT integration in building projects. Design/methodology/approach: A three-round Delphi method was used to identify, refine and validate critical BIM–ImT integration criteria. Eight experts from Australia and Vietnam participated in the process, with consensus defined as = 75% agreement and mean = 3.5 on a five-point Likert scale. Findings: The study confirmed 53 crucial criteria for BIM–ImT implementation: 27 technical criteria related to software compatibility, data synchronisation, visualisation capability and hardware specifications, and 26 non-technical criteria focused on user training and acceptance, organisational support, communication effectiveness and regulatory compliance. Practical implications: The findings provide a structured basis for planning BIM–ImT integration in building projects. Organisations should first establish foundational conditions, including interoperability, immersive performance, user training and collaborative workflows, before pursuing strategic investments. This helps practitioners assess readiness, sequence implementation and align decisions with technical and organisational dependencies. Originality/value: The study advances BIM–ImT research by differentiating technical and non-technical conditions underpinning successful integration. It contributes a socio-technical systems-informed framework that prioritises BIM–ImT criteria as an interdependent configuration of technical infrastructure, organisational processes and human capabilities across mature and emerging construction markets.

Research on industrialised building (IB) adoption has extensively documented technical, organisational and market barriers but has paid less attention to how responsibility for addressing them is organised. The purpose of this study is to examine how responsibility, trust and actor-positioned risk are constructed in Australian practitioner accounts of IB adoption. A critical discourse analysis of interviews with 23 practitioners provides the primary empirical evidence. A secondary corpus of English-language YouTube comments provides a bounded contrast with public discourse. Responsibility-void discourse was most evident around shared functions including training, certification, inspection, industry coordination and design-to-delivery integration. Trust helped interpret why some firms sought greater control over functions they considered insufficiently assured externally, while risk differentiated the delivery, compliance, capital and purchase-related forms of exposure described across the two corpora. The analysis also identified distinct information conditions, including knowledge-infrastructure absence and bounded cases of strategic opacity and interoperability failure. The illustrative public corpus foregrounded purchase conditions, tenure and category credibility, whereas practitioner accounts focused on institutional delivery and coordination. The study develops a framework for responsibility in IB adoption that distinguishes fragmented responsibility, responsibility shifting and responsibility voids. It conceptualises ownership as accountable coordination of work distributed across actors, reframing persistent adoption barriers as problems of institutional organisation, coordination and follow-through.

Automated clash detection in Building Information Modelling (BIM) has reached operational maturity; however, subsequent stages of clash management, including filtering, prioritisation, resolution, and prevention, remain largely dependent on manual expert judgement. Artificial intelligence (AI) has been increasingly applied to address these limitations, yet focused synthesis remains limited on how AI techniques support different stages of BIM-based clash management, what levels of automation they achieve, and how ready they are for professional deployment. This review follows PRISMA 2020 and synthesises 21 empirical studies published between 2019 and 2026, providing one of the first focused syntheses of AI applications across the BIM-based clash management lifecycle. Several AI technique families are identified, including rule-based and knowledge-based systems, classical supervised machine learning, deep learning and computer vision, graph-based and relational methods, and reinforcement learning and generative AI approaches. The results show that research is concentrated on filtering, while resolution and prevention remain underexplored despite their greater coordination significance. A structured automation taxonomy is developed, revealing that systems described as autonomous are evaluated only in simulated environments. The synthesis further indicates that progress is constrained primarily by structural rather than algorithmic limitations, as most studies rely on single-project validation, report performance without class-level metrics, and produce black-box outputs that cannot meet the accountability requirements of professional coordination practice. The primary barrier to deployment is therefore not model capability but the absence of shared benchmark datasets, rigorous cross-project validation, interpretable outputs, and sociotechnical integration with professional practice.

Building Information Modelling (BIM) is widely adopted to support construction safety management; however, its implementation remains predominantly technology-driven and inconsistently aligned with Lean Construction principles. This study examines how the conceptualisation of BIM shapes its alignment with Lean Construction principles, …

Purpose: The assessment and performance evaluation of photovoltaic (PV) systems depend heavily on the site characteristics of existing buildings. However, when physical site inspections are impractical or impossible due to restricted access, this creates a significant barrier to the assessment of PV layout design. This research uses an instrumental case-study methodology to optimise PV array design in existing buildings by integrating Building Information Modelling (BIM) with immersive technologies. Design/methodology/approach: A two-stage optimisation approach was developed to address challenges associated with physical site assessments and fixed building locations. By utilising optimisation algorithms, the grasshopper optimisation algorithm, and Unreal Engine 5 (UE5) for immersive visualisation, this method facilitates detailed shadow analysis and optimal placement of fixed-mounted PV panels. The methodology was validated through a case study involving the modelling of an office building in Melbourne, Australia, using Revit. A rigorous comparative analysis of the developed workflow and the Solar Engineer tool was conducted on the same building to assess the efficiency and accuracy of the workflow. Findings: Results demonstrated that integrating BIM with immersive technologies substantially enhanced the efficiency and accuracy of PV installation assessments. The analysis identified a roof utilisation factor (Roof UF) of 39.62% for PV panels, enabling the installation of 90 solar panels with an annual energy yield of 152,091.64 kWh. Originality/value: The optimisation workflow introduced in the paper highlights its potential to reduce the reliance on physical site visits while improving design precision and maximising energy output. These findings provide valuable insights for solar engineers and building professionals, contributing to the advancement of efficient and sustainable urban energy solutions.

Organizational learning refers to the systematic development, exchange and dissemination of knowledge throughout the organization. Organizational learning processes in construction are disrupted by the decentralized flow of information and the temporary, short-term nature of project teams. The emergence of Building Information Modelling (BIM) has significantly enhanced the ability to capture and disseminate construction project knowledge within the architecture, engineering, construction, and facilities management (AEC-FM) sector. Despite this progress, existing research has predominantly focused on the technical aspects of BIM, with limited evidence on its effects on organizational learning capabilities. This study addresses this gap by examining how BIM shapes organizational learning mechanisms within AEC-FM contexts. Employing a systematic literature review (SLR) approach, 104 articles from the Scopus database were analyzed using scientometric and thematic analyses. The systematic review of the literature was carried out following the PRISMA guidelines. The SLR provided a comprehensive examination of BIM’s contribution to strengthening the three core organizational learning mechanisms: experience accumulation, knowledge articulation, and knowledge codification. The thematic analysis revealed seven BIM-enabled organizational learning factors that are expected to strengthen learning mechanisms in AEC-FM organizations: agility of thinking and reasoning skills; enhanced decision-making; interconnected stakeholders’ relationships; integrated business processes; BIM-facilitated project knowledge sharing; BIM-supported project knowledge retention; and BIM-supported project knowledge extraction. Findings suggest that BIM significantly facilitates learning mechanisms within AEC-FM firms. A conceptual model of BIM-supported learning mechanisms was developed to highlight opportunities for enhancing organizational learning capabilities in the BIM environment.

This paper presents an Internet of Things (IoT)–enabled automated control framework for optimizing residential window ventilation systems. The system combines sensors, AI prediction, and automated motors to control ventilation based on environmental conditions and human behaviors. Unlike traditional fixed rule–based systems, our framework uses machine learning trained on 1.2 million data points to predict and automatically adjust ventilation beforehand. The modular automation system features message queuing telemetry transport (MQTT) and building automation and control network (BACnet) communication protocols for seamless integration with existing building management systems (BMSs), fail‐safe mechanisms for operational reliability, and mobile override capabilities preserving user autonomy. A 1‐month pilot study across 50 suburban Chinese households achieved an 18% reduction in heating, ventilation, and air‐conditioning (HVAC) energy use. The framework is modular and scalable, working with current building systems. It includes technical specs, integration methods, and security protections. While behavioral insights inform control logic, the core innovation lies in the automation architecture. This study advances intelligent building systems by fusing human‐centric design with robust automation engineering.