Purpose The study aims to develop an integrated framework to enhance the value engineering (VE) approach in construction by leveraging building information modeling (BIM) and artificial intelligence (AI). The framework focuses on material optimization and sustainable resource management while ensuring quality and cost-effectiveness to attain the circular economy (CE). Design/methodology/approach A systematic literature review, guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, is conducted to examine the applications of VE in construction. A mixed-method approach combines quantitative analysis, including keyword co-occurrence and clustering, with qualitative content analysis. The Delft Ladder approach is employed to structure the integration of VE with BIM and AI technologies, forming the foundation of the novel industrial practice-based framework. Findings The study reveals significant potential for enhancing VE through digital transformation. Integrating BIM and AI with VE principles demonstrates improved efficiency in material optimization and reduction of environmental impacts. The proposed Framework promotes closed-loop systems in construction by enabling data-driven decision-making, improving resource efficiency and allowing stakeholders to adopt CE principles throughout the construction lifecycle. Practical implications The framework offers construction professionals pragmatic solutions to mitigate embodied carbon, encourage material reuse and fulfill sustainability objectives. It tackles issues in conventional VE implementation by integrating digital technologies with CE procedures for efficient material management. Originality/value This research introduces an innovative framework that uniquely integrates VE principles with BIM and AI functionalities, employing the reduce, reuse and recycle methodology. The framework can enhance value and minimize expenses through optimization and material efficiency to achieve both functionality and cost-effectiveness.

Optimizing building information modeling through clash detection and resolution for sustainable high-rise construction

This study presents an integrated workflow for acquiring, processing, and fusing terrestrial laser scanning and Unmanned Aerial Vehicle (UAV) photogrammetric data to generate digital twins of heritage buildings within Heritage Building Information Modeling (HBIM) and Historical Geographic Information System (HGIS) environments. Using a historic wooden church as a case study, the proposed approach demonstrates improved completeness and geometric quality compared to UAV-only models. Dimensional differences between UAV-only and integrated models ranged from 0.8 to 3.2 cm, confirming internal consistency and suitability for documentation purposes. The workflow standardizes key stages of acquisition, scaling, and point cloud fusion, and establishes links between HBIM models at Level of Detail (LOD) 100–300 and conservation requirements. Additionally, it identifies integration points for Artificial Intelligence (AI)-based automation, supporting future developments in classification, segmentation, and conversion of 2D documentation into HBIM. The results highlight the potential of terrestrial laser scanning (TLS)-UAV integration for accurate, replicable heritage documentation and spatial–historical analysis.

ABSTRACT The growing complexity of spatial data management in Architecture, Engineering, and Construction requires integration of Building Information Modeling and Geospatial Information Systems. This study proposes a topological ontology that formally represents adjacency, intersection, and containment relationships to support spatial reasoning in built environments. Utilizing Semantic Web technologies, Industry Foundation Classes data are converted into Resource Description Framework to enhance interoperability and enable efficient spatial queries. A case study on a multi-story building demonstrates improved data management; RDF files are 12.8 times smaller than IFC files with sub-second performance for complex topological queries.

Abstract. This research aims to develop and apply a structured technology selection framework for initial data acquisition for Heritage Building Information Modeling (HBIM) and Digital Twin creation in Sri Lankan heritage sites. The study addresses the challenge of selecting appropriate 3D documentation technologies by proposing a transparent and systematic decision-making process. The study employs an Analytic Hierarchy Process (AHP) as its foundational framework. An AHP model was developed, defining a set of criteria (Cost, Geometric Accuracy, Texture Quality, Ground and Aerial Accessibility, Scale) and alternatives (various terrestrial and drone-based scanning and photogrammetry technologies). The framework’s effectiveness was validated through a detailed case study of Lankathilake Pilimage, followed by hypothetical applications for four other representative heritage sites. The AHP model successfully provided a ranked list of technology alternatives. For the Lankathilake Pilimage case study, the AHP results prioritized Drone Photogrammetry as the most suitable method, followed by Terrestrial Photogrammetry and then a combined Drone Lidar and Drone Photogrammetry approach. The application of the framework to other sites demonstrated how project- specific requirements, translated through pairwise comparisons, lead to different optimal technology choices. The AHP framework provides a robust and repeatable methodology for selecting primary data acquisition technology. It effectively incorporates multiple, often conflicting, criteria to arrive at a data-driven decision. This framework is a valuable tool for heritage conservation professionals and researchers, offering a clear path forward for HBIM and Digital Twin projects in Sri Lanka.

With the inclusion of ultra-high voltage and robust smart grid construction in the national development strategy, substations play a crucial role in building smart grids. However, substation construction faces challenges like short project cycles, high quality requirements, and poor cost control. Building Information Modeling (BIM) technology has been widely applied in construction and provides new solutions for substation building. This paper explores the 3D modeling and optimization methods for prefabricated substations using BIM. It focuses on the advantages of BIM in substation construction, discusses the processes and techniques of 3D modeling, and suggests optimization strategies like improving model precision, resource allocation, and construction efficiency. By analyzing and validating case studies, the paper shows how BIM improves efficiency, reduces costs, and enhances quality. The research offers a theoretical foundation, technical support, and practical guidance for constructing prefabricated substations, with significant application value and development prospects.

The assessment of Industrialized Building System (IBS) adoption in construction projects—a critical metric for evaluating prefabrication levels and construction modernization—remains largely manual, time-intensive, and prone to inconsistencies, with practitioners typically requiring 4–8 h to evaluate a single building using spreadsheet-based frameworks and visual documentation review. This paper presents a novel AI-enhanced workflow architecture that automates IBS scoring through systematic processing of Industry Foundation Classes (IFC) building information models—the first documented integration of web-based IFC processing, visual workflow automation (n8n), and large language model (LLM) reasoning specifically for construction industrialization assessment. The proposed system integrates a web-based frontend for IFC file upload and configuration, an n8n workflow automation backend orchestrating data transformation pipelines, and an Azure OpenAI-powered scoring engine (GPT-4o-mini and GPT-5-0-mini) that applies Construction Industry Standard (CIS) 18:2023 rules to extracted building data. Experimental validation across 136 diverse IFC building models (ranging from 0.01 MB to 136.26 MB) achieved a 100% processing success rate with a median processing duration of 61.62 s per model, representing approximately 99% time reduction compared to conventional manual assessment requiring 4–8 h of expert practitioner effort. The system demonstrated consistent scoring performance with IBS scores ranging from 31.24 to 100.00 points (mean 37.14, SD 8.84), while GPT-5-0-mini exhibited 71% faster inference (mean 23.4 s) compared to GPT-4o-mini (mean 80.2 s) with no significant scoring divergence, validating prompt engineering robustness across model generations. Processing efficiency scales approximately linearly with file size (0.67 s per megabyte), enabling real-time design feedback and portfolio-scale batch processing previously infeasible with manual methods. Unlike prior rule-based compliance checking systems requiring extensive manual programming, this approach leverages LLM semantic reasoning to interpret ambiguous construction classifications while maintaining deterministic scoring through structured prompt engineering. The system addresses key interoperability challenges in IFC data heterogeneity while maintaining traceability and compliance with established scoring methodologies. This research establishes a replicable architectural pattern for BIM-AI integration in construction analytics and positions LLM-enhanced IFC processing as a practical, accessible approach for industrialization evaluation that democratizes advanced assessment capabilities through open-source workflow automation technologies.

Abstract Unmanned Intelligent Construction in Railway Infrastructure (UIC/RI) is rapidly emerging in China, driven by the increasing complexity of infrastructure projects (e.g. railway tunnels in extreme geological conditions) and a shrinking labor force due to an aging society. UIC/RI utilizes unmanned or remotely operated construction machinery, integrated with comprehensive monitoring systems, to replicate and augment human perception and control. This paper reviews the application of advanced technologies in UIC/RI, including unmanned intelligent machinery, real-time perception (e.g. LIDAR, camera and radar etc.), Artificial Intelligence (AI), big data analytics, Building Information Modeling (BIM), and Digital Twins (DT). The review focuses on robotic autonomous operations and long-distance remote control, detailing unmanned systems for tunnels, and embankments. It also examines the role of monitoring and imaging technologies in enhancing construction safety. Finally, the paper outlines future research directions and prospects for the railway infrastructure.

Purpose This study addresses the critical need for energy efficiency in construction by developing and validating an intelligent, integrated framework to support informed material selection during the early design phase. The primary objective is to integrate Building Information Modeling (BIM), Augmented Reality (AR), and automated energy analysis to identify and recommend materials that minimize operational energy costs, tailored to a project’s specific location. Design/methodology/approach The methodology establishes a cohesive digital workflow. A BIM model is created in Autodesk Revit and analyzed using Autodesk Insight Carbon Analysis for energy performance. A custom C# plugin, the Energy and Cost Analysis of Materials (ECAM), automates the processing and ranking of energy data. The results are then visualized in an immersive AR environment developed in Unity 3D, enabling interactive, on-site decision-making. Findings The ECAM framework successfully translates complex energy simulation data into a clear, ranked list of material options. In a case study of a residential building in Chabahar, Iran, wood emerged as the most energy-efficient façade material, offering a potential annual operational energy cost saving of 9.5% compared to ceramic. The AR interface proved to be an effective medium for designers and stakeholders to visualize material trade-offs in context, enhancing decision quality and communication. Originality/value This research presents a novel framework bridging abstract energy analysis and practical design application. Its originality lies in combining BIM, a custom automation plugin (ECAM), and an immersive AR interface into a single workflow. The framework provides designers and clients with a practical, data-driven tool to optimize material selection, reduce long-term operational costs, and advance sustainable building practices.

ABSTRACT The management of historic buildings goes beyond technical expertise or theoretical knowledge, requiring not only the integration of multiple professional competences but also the ability to tackle the practical challenges that arise in everyday conservation practice. Planned conservation focuses on the physical preservation of the asset through proactive strategies such as monitoring, maintenance, and condition assessment. Its implementation demands visual support, integration of expertise, process automation, and collaborative tools. Data management platforms can support this effort, with digital technologies enabling their effective creation and use. This paper presents a methodology for developing a digital platform to support the planned conservation of the Cathedral of Santa Maria Assunta in Pisa (Italy). The approach, integrating Building Information Modelling and Geographic Information System techniques, delivers an interactive three-dimensional model published via cloud-computing services. By integrating multiple representation levels within a unified digital environment, the platform addresses the challenge of integrating different disciplines taking into account variable levels of refinement. It ensures flexible, long-term conservation data management, and can be easily adapted to other cultural heritage assets.

ABSTRACT Building structural designs, utilizing materials such as steel, concrete, and cross-laminated timber, contribute significantly to embodied carbon emissions in construction projects. However, traditional carbon accounting methods employed to quantify and record these emissions are often characterized by a lack of traceability, transparency, and immutability. This limitation undermines the reliability of emissions data, making it challenging for stakeholders to establish credible emissions records and implement regulatory strategies, such as carbon credits, taxes, subsidies, and green certifications, for building’s structural designs and materials. This paper addresses these challenges by proposing a transformational emissions accounting system that integrates Building Information Modeling (BIM) for automatic extraction of emissions-relevant data, alongside blockchain-enabled smart contracts to ensure traceability and immutability of emissions records. The proposed system enables data-driven decision-making for low-carbon structural designs and materials, while also facilitating the application of emissions regulations to support their implementation based on trustworthy emissions accounting.

Garden landscape designing software has revolutionized the way landscape architects, designers, and homeowners conceptualize and execute outdoor spaces. These digital tools provide advanced features for planning, visualizing, and managing garden layouts, incorporating elements such as plant selection, hardscaping, irrigation systems, and lighting. With 3D modeling, real-time rendering, and integration with Building Information Modeling (BIM), modern software enhances design accuracy and project efficiency. Applications like AutoCAD, SketchUp, and specialized tools such as Realtime Landscaping and PRO Landscape cater to both professionals and hobbyists, offering customizable templates and AI-driven design suggestions. Additionally, environmental and sustainability considerations are increasingly integrated, allowing designers to assess factors like water conservation, soil health, and climate adaptability. As garden landscape software continues to evolve, it plays a crucial role in sustainable urban planning, smart gardening solutions, and the seamless execution of aesthetically pleasing, functional, and eco-friendly outdoor environments.

ABSTRACT The implementation of artificial intelligence (AI) and machine learning (ML) in the architecture, engineering, and construction (AEC) domain has gained significant attention within academic research and construction technology companies. The scientific field often relies on information collected from the industry for theoretical development and sometimes test cases. However, to the best of the authors’ knowledge, there has been no systematic effort to compare academic and industry trends and developments. In this work, we examine the development and application of ML in the AEC domain from both academic and industry perspectives. To investigate both perspectives, we implement a mixed-methods approach including an academic literature review, a web-based mapping of construction technology companies worldwide, and an online questionnaire targeting practitioners and technology companies. Overall, our mixed methods analysis reveals a strong alignment between academic research and industry practice in targeting early design and post construction phases, with both communities prioritizing the application of ML for energy efficiency, facility management, and site safety, particularly within the context of Building Information Modeling (BIM). However, industry devotes more effort to sustainability, cost and scheduling solutions, whereas academia lags in these areas and often relies on limited or synthetic datasets. Finally, both sectors identify data availability and quality, particularly the scarcity of large, labeled, domain-specific repositories, as the primary barrier to wider ML adoption in the AEC industry.

The construction industry has been rapidly digitalized, and thus, the process of the use of Building Information Modelling (BIM) and Digital Twin technology has increased in the number of projects being monitored and controlled. Nevertheless, traditional instances of digital twins can be described as visualization platforms that have a short analytical intelligence and lack decision transparency. This paper presents an Explainable Artificial Intelligence-powered BIM- based Digital Twin system of real-time construction monitoring. The scheme combines input data of multi-source construction, such as site progress, IoT sensor feeds, and scheduling in a synchronized digital twin environment. There is a predictive analytics engine built on AI, which predicts delays, safety issues, and productivity variability, as well as an explainable decision interpretation that increases the transparency of the autopilot analysis and recommendations. It is also backed by interactive visualization and decision support interface to help the project stakeholders in proactive planning and mitigation of risks. The results of implementation indicate that there are better monitoring accuracies, quick site dynamics, and increased interpretability in project control procedures. The paper confirms that incorporateability of explainable intelligence into digital twins, generated through BIM, can enhance data-based decision-making, responsibility and operational performance of complex civil infrastructure developments to a large extent.

Purpose The construction supply chain (CSC) faces persistent inefficiencies, opacity, and fragmented collaboration. Blockchain technology (BT) has been proposed as a remedy, yet adoption in construction supply chain management (CSCM) remains limited. This study synthesises current knowledge on blockchain-enabled CSCM, focusing on applications, adoption barriers, integration with complementary technologies, and future research directions. Design/methodology/approach A scientometric analysis was employed to examine 145 systematically filtered publications on blockchain-enabled CSCM, providing a critical understanding of research trends, thematic structures, and knowledge developments. A content analysis of 40 selected high-impact articles was conducted to elucidate key applications, challenges, integration pathways, and future research directions. Findings BT’s main applications in CSCM include enhancing transparency and traceability, automating payments through smart contracts, and enabling real-time collaboration when integrated with Building Information Modeling (BIM), Internet of Things (IoT), and digital twins. Adoption, however, is constrained by interoperability challenges, high implementation costs, data privacy concerns, regulatory gaps, and organisational resistance. Research remains fragmented, with limited cross-disciplinary collaboration and a lack of large-scale empirical validations. Originality/value This study synthesises blockchain-CSCM literature using a mixed-method scientometric and content analysis approach, offering a comprehensive thematic synthesis that bridges conceptual propositions with practical adoption challenges. The study advances both academic discourse and practical implementation, guiding the construction industry toward more transparent, efficient, and resilient supply chains.

The design of thermal strategies applied in buildings based on the use of renewable energies can play an important role in the development of a built environment that is better adapted to the climate. This paper is focused on the application of a renewable solar energy system coupled with a Heating, Ventilation and Air-Conditioned (HVAC) system to promote occupants’ thermal comfort (TC) and indoor air quality (IAQ) in buildings during heating season. In the building thermal design, a building thermal dynamic model is used to calculate the temperatures of the opaque and transparent building surfaces, the temperature of the water supply ducts, the TC level and the IAQ level, among other variables. The TC conditions of the occupants were evaluated using the Predicted Mean Vote index, commonly used in the literature in similar studies. IAQ was assessed by the usual carbon dioxide concentration in environments where most of the pollution is of human origin. The numerical study was carried out in a virtual residential building consisting of two floors and seven compartments. The building is occupied at night and at midday. Two cases were studied, considering, respectively, the non-use and use of the solar HVAC system. The solar HVAC system consists of solar water collectors, installed above the roof area, and thermo-convector heat exchangers, installed inside each occupied space. The results show that the application of this solar HVAC system in a Mediterranean-type climate is able to guarantee, during occupancy, acceptable TC levels in three compartments and near acceptable TC levels in one compartment. Regarding IAQ, acceptable level can be achieved throughout the day.

Purpose This study develops a structured theoretical model explaining how the AEC sector can progress from current building information modelling (BIM) practices to system-of-systems (SoS) digital twins through the evolution of BIM to Stage 3, enabled by big BIM data (BBD). Grounded in contemporary standards rather than legacy BIM concepts, the model conceptualises BBD and BIM Stage 3 as drivers of advanced interoperability, lifecycle integration and real-time decision intelligence, providing a foundation for future empirical validation. Design/methodology/approach The study triangulates evidence from a systematic literature review, a bibliometric analysis and semi-structured expert interviews. The review identifies conceptual gaps, while the bibliometric analysis maps research trends alongside the interviews, incorporating insights from academia, industry and software development. A deductive thematic analysis integrates these findings. Findings The results indicate fragmented organisational processes, technology-rich but poorly integrated IT infrastructures and limited strategic alignment with the capabilities required for data-driven value creation. Five bibliometric clusters combined with interview insights reinforce three Strategic Alignment Model domains while revealing persistent weaknesses in organisational strategy, workforce capability and regional digital maturity. Practical implications The study engenders a theoretical contribution by proposing an integrated framework that addresses how progress towards SoS digital twins within BIM Stage 3 depends on vertical alignment between strategy and operations, horizontal interoperability across the supply chain and end-to-end information continuity. On top of that, a maturity assessment matrix based on four SAM domains supports practical evaluation of readiness. Originality/value The study reframes BIM Stage 3 through a standards-aligned, theory-building perspective. It develops a three-dimensional integration framework and a practitioner-focused maturity matrix, offering a coherent conceptual foundation and a diagnostic tool to support future empirical research and sector-wide digital transformation.

Rework represents a significant challenge to the successful delivery of bridge projects, with substantial implications for cost, schedule, and resource efficiency. This study investigates the impact of Building Information Modeling (BIM) on reducing rework in bridge construction, with particular emphasis on rework ratio, cost, and schedule performance. The research methodology combined pre- and post-interviews, data analysis, and a comparative evaluation of projects executed with and without BIM. The results suggest that BIM adoption can reduce rework-related inefficiencies, with observed reductions in time wastage of approximately 70-85% and cost savings in the range of 65-75% in the analyzed case study. Furthermore, Earned Value Management (EVM) analysis revealed improved performance metrics, with Schedule Performance Indicator (SPI) and Cost Performance Indicator (CPI) values increasing by 0.264 and 0.216, respectively. These findings provide indicative evidence that BIM has the potential to enhance cost and schedule outcomes in bridge construction by mitigating rework, though outcomes are context-dependent and may vary according to project scale, scope, and implementation practices. The study contributes to the growing body of knowledge on BIM-enabled project management and offers practical insights for industry practitioners seeking to improve project delivery effectiveness.

The growing complexity of construction projects has revealed significant limitations in traditional planning and scheduling methods with regard to predictability, cost control, and productivity. Four-dimensional Building Information Modeling (4D BIM), which integrates time-related information into three-dimensional digital models, has emerged as an effective approach to overcome these limitations. This study examines the impacts of 4D BIM integration on construction management, with emphasis on schedule reliability, decision-making processes, and productivity enhancement. Based on a critical review of academic literature, the analysis demonstrates that 4D BIM improves visualization of construction sequences, enables scenario-based planning, strengthens coordination among project stakeholders, and supports proactive control of time- and cost-related risks. Despite existing implementation challenges, the findings indicate that 4D BIM constitutes a strategic tool for improving overall project performance and supporting the digital transformation of the construction industry.

As the digitalization of construction standards accelerates, the integration of structural analysis results into Building Information Modeling (BIM) environments has become a critical prerequisite for effective BIM-based Automated Code Checking (ACC), particularly for structural code compliance. In current practice, structural analysis results generated by Computer-Aided Engineering (CAE) tools are often manually transferred into IFC-based BIM models, leading to inefficiencies and increased risk of human error. To address this limitation, this study proposes an extended IFC-based representation, termed IFC-KR-Structure, designed to systematically organize and manage section-wise and load combination-dependent structural analysis results required for code compliance within the IFC environment. Based on the proposed schema, an automated CAE-to-BIM integration module was implemented within the IFC-KR Toolkit to enable direct integration of analysis results generated by a commercial CAE tool (midas Civil NX) into IFC models. The approach establishes consistent element correspondence between structural and BIM models through coordinate alignment and spatial mapping procedures and represents multidimensional analysis results using a schema-compliant, tabular data structure embedded within IFC models. The applicability of the proposed framework was validated using a prestressed concrete girder bridge case, confirming that structural analysis results were accurately mapped, stored, visualized, and subsequently utilized within a BIM-based ACC workflow. The results demonstrate that the proposed approach enables systematic reintegration of CAE-generated analysis results into BIM models and significantly improves the efficiency, consistency, and reliability of BIM-based code compliance processes.