Purpose This study aims to comprehensively examine the integration of artificial intelligence (AI) and building information modeling (BIM) within the building construction field and identify four key enablers to develop PERGE as an AI–BIM adoption framework. It aims to evaluate the applicability of AI methods, including generative methods, and identify emerging trends and underexplored combinations of AI methods and use cases. Design/methodology/approach A scientometric methodology was adopted to establish the AIBI dataset, including 971 peer-reviewed publications, and analyze them based on a computational review and evidence gap maps (CEGMs) approach. A structured query was designed to identify relevant investigations, which were then analyzed to map publication trends, identify dominant AI applications in buildings, perform temporal analysis of recent developments and develop a construct–outcome heatmap. Findings The analysis reveals a significant evolution in AI–BIM integration for buildings, with a shift from early automation tasks to more advanced objectives such as generation, prediction and semantic understanding. There is a notable rise in the use of large language models, reinforcement learning and fine-tuned transformers. The study also identifies a transition in methodological focus from general prediction tasks to the development of algorithmic frameworks tailored to facility needs. Generative AI has notably influenced expectations and applications in the field while also exposing gaps in underutilized areas. Originality/value This paper provides a novel, data-driven synthesis of AI–BIM integration investigations in building construction, energy and facility management, with a particular emphasis on the transformative role of generative AI. The novel adoption framework of PERGE is established, offering valuable insights for researchers and practitioners along with the identification of key trends, suitable AI methods and underexplored opportunities.

Incomplete survey data often undermines the reliability of Building Information Models (BIM), particularly for structures with restricted access and complex geometries. This study demonstrates a hybrid Scan-to-BIM workflow that integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, supported by a predictive feasibility concept, to improve documentation accuracy and completeness. A two-phase strategy was validated on a chapel case study. Phase 1, combining TLS and ground-based photogrammetry, achieved only 54% coverage due to severe occlusions and limited scanner placement. These results led to the formulation of a Predictive Scan Feasibility Estimation Model (PSFEM), designed to generalize site-specific parameters such as scanner range, clearance angle, and building height into a decision-support tool for future surveys. Guided by the recognition of Phase 1 limitations, Phase 2 incorporated UAV photogrammetry and supplemental TLS, increasing coverage to 96%. Comparative analyses confirmed consistency in accuracy and improved geometric completeness. While the PSFEM was developed retrospectively based on the limitations identified in Phase 1, its analytical validation demonstrates the potential value of predictive planning for reducing redundant site visits and enhancing BIM reliability. The proposed framework provides a transferable basis for applying predictive hybrid workflows in both heritage and complex building documentation. This workflow offers a practical and scalable method for Scan-to-BIM documentation, applicable to heritage as well as other complex buildings, enabling high accuracy and completeness while effectively managing time and resources.

Abstract This study examines the potential of light‐transmitting concrete to reduce energy consumption in building construction. A series of tests, including compressive strength and light transmittance assessments, were conducted, alongside modeling a residential building using building information modeling software. Light‐transmitting concrete samples were prepared using single‐mode optical fibers, plastic optical fibers, and waste‐tempered glass. The results demonstrated that light‐transmitting concrete with 1% volumetric single‐mode optical fibers achieved a 28‐day compressive strength of 39.2 MPa and 2% light transmission. Light‐transmitting concrete containing 5% volumetric plastic optical fibers also showed 5.88% light transmission and a 28‐day compressive strength of 44.89 MPa. The sample incorporating 14% by weight of broken tempered glass exhibited a compressive strength of 51.1 MPa with 1.15% light transmission. A two‐story residential building (1200 m 2 ) in Tehran was analyzed in the modeling phase, integrating light‐transmitting concrete blocks in a residential building, while solar panels are considered only as a complementary reference for contextual energy and economic evaluation. Energy analysis and return on investment calculations revealed that the optimal setup involved a combination of light‐transmitting concrete, conventional concrete, with an estimated return on investment period of 5.22 years. Finally, an economic analysis is performed to demonstrate how integrating a photovoltaic system can offset the higher initial cost of LTC blocks, reducing the payback period to a feasible range.

Metal components, due to their high prefabrication rate and discrete manufacturing characteristics, have become an ideal carrier for verifying intelligent construction technologies. However, traditional modeling methods face bottlenecks such as low efficiency, poor robustness, and semantic gaps. This study systematically reviews the research progress of Building Information Modeling (BIM) and Artificial Intelligence (AI) in the automated modeling and life-cycle management of metal components. First, it elucidates the semantic support for metal structures in the IFC 4.3 standard and the theoretical basis of BIM-AI integration. Then, from a forward design perspective, it reviews parametric modeling, deep learning topology optimization, and generative adversarial networks (GANs), while from a reverse reconstruction perspective, it outlines point cloud semantic segmentation and Scan-to-BIM automation technologies. Further, it explores intelligent management methods such as knowledge graph compliance checks, digital twin frameworks, and visual defect detection. Finally, it points out key challenges and future development directions, including data heterogeneity, scarce annotations, and interpretability.

Building Information Modelling (BIM) is driving transformation in the built environment. While technical aspects have been widely explored, less is known about how cloud-based BIM (CBIM) supports business strategy and growth. This gap poses a challenge for organisations aiming to integrate digital innovation with competitive advantage. This study develops a CBIM business model framework, identifying strategic drivers for business expansion. Unlike prior BMC adaptations, the proposed framework integrates both microeconomic dimensions (e.g., pricing logic, competition, diversification) and macroeconomic dimensions (e.g., governance, sustainability, reskilling), explicitly linking firm-level strategies with broader institutional and digital infrastructure factors. A qualitative multi-case study was conducted, involving nine semi-structured interviews with CBIM experts across Germany, Finland, the Netherlands, and the UK. Participants represented both BIM consumers (e.g., construction companies and consultants) and BIM producers (e.g., software vendors and platform providers). Purposeful sampling ensured sectoral and role diversity. Data was coded and analysed thematically using Atlas. ti qualitative analysis software. The study identifies nine strategic drivers for CBIM business growth, including a redefined value proposition, customer awareness, cloud infrastructure, product and service pricing logic, distribution channels, human resource capabilities, strategic use of competition, diversification, governance and sustainability. The study proposes a CBIM-adapted Business Model Canvas framework, providing actionable insights for startups and SMEs, including guidance on integrating cloud pricing models and aligning internal capabilities with digital strategy such as guidance on integrating cloud pricing models and aligning internal capabilities with digital strategy, while also highlighting strategies that incumbents can adopt to maintain competitiveness in a rapidly digitising construction ecosystem.

This study proposes a sustainable urban planning strategy that enhances building energy self-sufficiency through photovoltaic-based renewable energy generation. This research focused on high-rise residential complexes and introduces an adaptive facade system designed to efficiently utilize the extensive solar-exposed surfaces of building facades. The system was programmed to automatically adjust according to the optimal tilt angle, which reflects monthly variations in solar altitude and azimuth, thereby maximizing energy generation. To further improve the effectiveness of the adaptive facade system, a “bridge-type apartment complex” layout was developed, in which building orientations varied around a south-facing axis to include southeast and southwest orientations. The proposed urban configuration was developed using a parametric design within the building information modeling software Revit 2023, and energy generation simulations were conducted for Seoul, South Korea, using Insight (a Revit plug-in) during 2024. Simulation results revealed that bridge-type apartment complexes achieved higher levels of renewable energy generation than conventional slab-type apartment complexes. These findings suggest that a three-dimensional urban design incorporating diverse facade orientations, combined with a dynamic adaptive facade system, not only enhances energy efficiency but also offers the potential to create creative and flexible urban landscapes that contrast with conventional, uniform cityscapes.

LEGO Voxelization Method for Arbitrary Building Information Models to Support Path Planning

The construction industry confronts persistent challenges related to inefficiencies, rework, and cost overruns, driving the need for advanced digital solutions like Building Information Modelling (BIM). This research appraises BIM clash detection's financial and operational benefits, emphasizing its pivotal role in improving project performance. Centered on case study projects, the research employs a comprehensive cost-benefit analysis framework, integrating quantitative data from BIM clash reports, project rework logs, and qualitative insights from stakeholder interviews. The analysis evaluates financial indicators such as Net Present Value (NPV), Benefit-Cost Ratio (BCR), and Return on Investment (ROI) to determine the viability of BIM clash detection. Findings reveal that implementing this technology lessens rework frequency, enhances project timelines, and fosters stakeholder communication and coordination. A significant reduction in errors and rework also ensures higher cost savings and more efficient resource utilization. The study utilizes advanced techniques like federated BIM modelling, sensitivity analyses, and scenario-based evaluations to simulate real-world conditions and quantify outcomes. Results confirm a positive NPV, a BCR greater than 1, and a high ROI, underscoring BIM clash detection's economic feasibility and long-term value. The research illustrates how this technology mitigates construction risks, improves stakeholder satisfaction, and ensures superior project delivery quality. Through its rigorous methodological approach and robust analysis, this research demonstrates the transformative potential of BIM in modern construction. It offers actionable insights for stakeholders seeking to enhance efficiency, reduce costs, and adopt innovative technologies to revolutionize project delivery and management processes.

Digital transformation is transforming construction project management by redefining how information is created, processed and shared. Although Building Information Modelling (BIM), Artificial Intelligence (AI) and Project Management Information Systems (PMIS) have each shown value in improving efficiency, their combined effect on sustainability has not been well theorized. This extended conceptual paper responds to this gap by examining how the three technologies interact through data integration, predictive analytics and structured coordination to enhance environmental, economic and social performance. The discussion synthesizes recent research, highlights impact mechanisms, and clarifies organizational conditions for achieving sustainability outcomes.

Abstract Building Information Modelling (BIM) is increasingly adopted in geotechnical engineering but remains hindered by the lack of standardised modelling methods and functional data structures. This paper presents a novel, generalisable BIM data model for tension-supporting elements (e.g., anchors, soil nails, rock bolts), which are used in almost every geotechnical project. Unlike previous efforts that focused primarily on construction-stage documentation, this study advances the state-of-the-art by integrating the full project lifecycle, including design, installation, inspection, and maintenance. The proposed data structure defines Level of Development (LOD) requirements for both geometry and metadata, aligned with project phases and maintenance needs. Three real-world cases from Norwegian infrastructure projects, covering tunnels, slopes, and foundations, form the basis for the proposed model, ensuring practical relevance and adaptability. The data structure is expandable so that maintenance-related information at different periods can be appended and back-traced. Even though realisation and testing in real projects are necessary, the proposed data structure is shown to be compatible with parametric design, the most widely used LOD frameworks, and common data exchange formats, e.g. “Industry Foundation Class” (IFC) for BIM. The current work is presented as a conceptual framework with full validation to be carried out in future infrastructure projects.

Conventional uses of building information modeling (BIM) in existing-building representation tend to prioritize geometric consistency and efficiency, but often at the expense of interpretive depth. This paper challenges BIM’s tendency to promote epistemic closure by proposing a method to foreground relational ambiguity, transforming view reconciliation from a default automated process into a generative act of critical inquiry. The method, implemented in Autodesk Revit, introduces a parametric reference frame within BIM sheets that foregrounds and manipulates reciprocal relationships between orthographic views (e.g., plans and sections) to promote interpretive ambiguity. Through a case study, the paper demonstrates how parameterized view relationships can resist oversimplification and encourage conflicting interpretations. By intentionally sacrificing efficiency for epistemic resilience, the method aims to expand BIM’s role beyond documentation, positioning it as a tool for architectural knowledge production. The paper concludes with implications for software development, pedagogy, and future research at the intersection of critical representation and computational tools.

The construction industry is undergoing a rapid digital transformation, with Digital Twins (DTs) and Extended Reality (XR) as two emerging technologies with great potential. Despite their potential, there are several challenges regarding DT and XR use in construction projects, including implementation barriers, interoperability issues, system complexity, and a lack of standardized frameworks. This study presents a systematic literature review (SLR) of DT and XR technologies—including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)—in the construction industry. The study analyzes 52 peer-reviewed articles identified using the Web of Science database to explore thematic findings. Key findings highlight DT and XR applications for safety training, real-time monitoring, predictive maintenance, lifecycle management, renovation or demolition, scenario risk assessment, and education. The SLR also identifies core enabling technologies such as Building Information Modeling (BIM), Internet of Things (IoT), Big Data, and XR devices, while uncovering persistent challenges including interoperability, high implementation costs, and lack of standardization. The study highlights how integrating DTs and XR can improve construction by making it smarter, safer, and more efficient. It also suggests areas for future research to overcome current challenges and help increase the use of these technologies. The primary contribution of this study lies in deepening the understanding of DT and XR technologies by examining them through the lenses of their benefits as well as drivers for and challenges to their adoption. This enhanced understanding provides a foundation for exploring integrated DT and XR applications to advance innovation and efficiency in the construction sector.

Purpose This study aims to identify key factors influencing digital twin implementation in construction and to validate their interrelationships using partial least squares structural equation modelling (PLS-SEM). Design/methodology/approach A structured, multi-phase research approach was adopted. Key constructs were identified through literature review and expert input, followed by the design of a questionnaire administered to 436 construction professionals. Exploratory factor analysis was used to refine constructs, and PLS-SEM was applied to test relationships. Second-order constructs were modelled to evaluate the integration of computer vision (CV), Internet of Things (IoT), digital technology integration and data-driven project performance within a unified framework. Findings The results demonstrate that CV–IoT integration, digital technology adoption and data-driven performance significantly contribute to real-time construction monitoring. Key relationships – such as CV–IoT to edge processing (ß = 0.754), digital integration to labour, material, equipment and activity (LMPA) monitoring (ß = 0.832) and data-driven performance to real-time monitoring (ß = 0.748) – confirm the model’s strength. These findings underscore the value of integrated digital systems in enhancing site visibility, progress tracking and predictive decision-making. Practical implications The model identifies critical digital and operational factors essential for structured digital twin prototype development, enabling real-time monitoring of LMPA to support automated tracking and improve cost, time and resource efficiency in construction projects. Originality/value This study develops a PLS-SEM-based digital twin framework that systematically integrates CV–IoT, edge processing, building information modelling and data-driven performance. The model provides a structured understanding of how these technologies collectively enhance productivity monitoring, progress assessment and project control in construction.

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects.

Building Information Modeling (BIM) is a digital approach capable of integrating planning, design, and construction information into a single unified model. The implementation of BIM in infrastructure projects in Indonesia continues to grow; however, its application at the tender stage is still not optimal. This study aims to analyze the implementation of BIM in the tender process of infrastructure projects and to identify the benefits and challenges encountered. The research method used is a qualitative descriptive method with a literature review approach and an analysis of BIM implementation practices at the tender stage. The results indicate that the use of BIM can improve the accuracy of tender documents, enhance efficiency in document preparation time, and increase transparency in the bid evaluation process. Nevertheless, BIM implementation still faces challenges, including limited human resources, high initial investment costs, and the absence of uniform national standards. This study is expected to serve as a reference for policy development and the implementation of BIM in the procurement process of infrastructure projects.

The article examines the specifics of the use of building information modeling technologies in the construction of a modern Allegro railway wheel manufacturing plant located in the Titanium Valley special economic zone in Verkhnyaya Salda. The prerequisites for the introduction of BIM for this largescale industrial project, the methodology of its use at the design stage, including the choice of software and the organization of collaboration, are considered. The advantages achieved through BIM are analyzed in detail, such as improving the quality of design solutions, effective coordination of sections, and speeding up the release of documentation. A mathematical model for estimating the economic effect of collision avoidance is presented, based on average estimated data, confirming significant cost savings. Special attention is paid to the potential of using the created BIM model at the plant's operational stage as the basis for a digital twin that helps optimize maintenance, resource management, and incident response. The experience of the Allegro project demonstrates the high efficiency of BIM technologies for the implementation of complex industrial facilities, confirms their role as a tool for digitalization of the construction industry and is a successful example for replication in other large investment projects.

Abstract The adoption of digital methods and tools, such as Building Information Modeling (BIM), holds significant potential for the real estate and construction industry. However, its implementation is often hindered by increasing complexity, insufficiently standardized processes, and knowledge gaps. Previous approaches to overcoming companies’ reluctance to adopt BIM have primarily focused on technological solutions and process-oriented methods. Nevertheless, the integration of the employees is on key success factor. To address these challenges more effectively, it is essential to develop strategies that emphasize the human factor and incorporate change management practices. This paper introduces a novel integration of archetypal theory into BIM change management, providing a unique lens to understand and address human dynamics during implementation. Based on a comprehensive literature review and expert interviews, archetypes influencing BIM adoption are identified, their organizational positions mapped, and their behaviors in response to change management processes analyzed using Mayring’s qualitative content analysis. This study proposes tailored strategies to engage and support these archetypes, marking the first integration of technical innovation with social adoption in BIM implementation. The findings demonstrate that successful BIM implementation requires a holistic change management approach that combines technical solutions with human-centered change management strategies. Early employee involvement, transparent communication, pilot projects and targeted engagement of key archetypes – such as the “friend” of “explorer” – enhance acceptance, trust, and motivation. This study is the first to explicitly integrate archetypal insights into BIM change management, offering both a theoretical framework and practical guidance to overcome adoption barriers and enhance BIM’s potential.

The construction industry faces some of the highest occupational safety and health (OSH) risks, driven by hazardous behaviors, unsafe conditions, and limited adoption of technology and training. This study employs a systematic literature review (SLR) of recent publications to evaluate the trends, benefits, and challenges of applying Internet of Things (IoT) and wearable devices in construction OSH management. The findings indicate that technologies such as smart helmets, sensor vests, and biometric wristbands enable real-time monitoring, early risk detection, and predictive safety management, reducing reliance on traditional inspection methods. Furthermore, the integration of IoT with Artificial Intelligence (AI), Big Data Analytics, Building Information Modeling (BIM), and Industry 5.0 principles enhances predictive capacity, infrastructure resilience, efficiency, and sustainability of construction projects. Despite these benefits, barriers including data privacy concerns, high implementation costs, interoperability issues, and shortages of skilled personnel remain, especially in developing countries such as Indonesia. Overall, IoT and wearable technologies demonstrate significant potential to transform OSH practices in the construction sector by improving safety, productivity, and sustainability. However, their broader adoption requires comprehensive strategies, including worker training, strong data protection policies, regulatory support, and participatory approaches to ensure effective and sustainable implementation.

The increasing role of automation systems in energy-efficient buildings creates a need for simulation approaches that support standardized assessment already at the design stage. This paper presents a digital twin-based simulation framework that integrates building information modeling (BIM)-derived building data with MATLAB/Simulink models to enable regulation-oriented evaluation of building automation and control strategies. The proposed approach targets scenario-based analysis of automation maturity levels, covering conventional, advanced, and predictive configurations aligned with EN ISO 52120 and the Smart Readiness Indicator (SRI). A representative academic building model is used to demonstrate how the framework supports reproducible modeling of heating, ventilation, and air conditioning (HVAC), lighting, and shading control functions and enables consistent comparison of their energy-related behavior under unified boundary conditions. The results show that the framework effectively captures performance trends associated with increasing automation sophistication and reveals interaction effects between control subsystems that are not accessible in conventional energy simulation tools. The proposed methodology provides a practical and extensible foundation for early-stage, regulation-aligned evaluation of smart building solutions and for the further development of predictive and artificial intelligence (AI)-assisted control concepts.

In Building Information Modeling (BIM) of bridge piers, persistent limitations have been observed in the modeling of spiral hoop rebar with variable pitch and diameter. Taking Revit as an example, its built-in family files can only generate spirals with constant geometry. When dealing with non-uniform rebar, designers often have to rely on segmented modeling or manual operations, which is not only time-consuming but also prone to deviations. To solve this problem, this paper proposes a parameterized modeling method based on the secondary development of Revit. By combining the Revit API with the C# programming language, the spiral equation is embedded into the Non-Uniform Rational B-Spline (NURBS) curve reconstruction framework, realizing the continuous modeling of spiral hoop rebar in a unified model. This method also allows users to flexibly input parameters such as cover thickness, rebar diameter, and segment length through a graphical user interface. Through comparative experiments, the proposed method and the traditional family file modeling method were verified respectively in the modeling of a single column and an entire bridge pier. The results indicate that the proposed method reduces the average modeling time of a single bridge pier by 66.5% and that of the entire project by 48.7%. While maintaining high geometric accuracy, this method significantly shortens modeling time and reduces workload, especially demonstrating higher consistency in pitch transition sections and conical sections. Beyond technical performance, this study also demonstrates that the secondary development of Revit provides a practical and feasible solution for the efficient, precise, and generalizable modeling of complex reinforcing bar components in terms of expanding BIM functions, which holds significant practical implications.