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The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

The construction industry is tasked with creating sustainable, efficient and cost-effective buildings. This study aims to develop a building information modeling (BIM)-based multiobjective optimization (MOO) model integrating the nondominated sorting genetic algorithm III (NSGA-III) to enhance sustainability. The goal is to reduce embodied energy and cost in the design process.

Through a case study research method, this study uses BIM, NSGA-III and real-world data in five phases: literature review, identification of factors, BIM model development, MOO model creation and validation in the architecture, engineering and construction sectors.

The innovative BIM-based MOO model optimizes embodied energy and cost to achieve sustainable construction. A commercial building case study validation showed a reduction of 30% in embodied energy and 21% in cost. This study validates the model’s effectiveness in integrating sustainability goals, enhancing decision-making, collaboration, efficiency and providing superior assessment.

This model delivers a unified approach to sustainable design, cutting carbon footprint and strengthening the industry’s ability to attain sustainable solutions. It holds potential for broader application and future integration of social and economic factors.

The research presents a novel BIM-based MOO model, uniquely focusing on sustainable construction with embodied energy and cost considerations. This holistic and innovative framework extends existing methodologies applicable to various buildings and paves the way for additional research in this area.

The purpose of this study is to explore the suggestions that construction processes could be considerably improved by integrating building information modelling (BIM) with 3D laser scanning technologies. This case study integrated 3D laser point cloud scans with BIM to explore the effects of BIM adoption on ongoing construction project, whilst evaluating the utility of 3D laser scanning technology for producing structural 3D models by converting point cloud data (PCD) into BIM.

The primary data acquisition adopted the use of Trimble X7 laser scanning process, which is a set of data points in the scanned space that represent the scanned structure. The implementation of BIM with the 3D PCD to explore the precision and effectiveness of the construction processes as well as the as-built condition of a structure was precisely captured using the 3D laser scanning technology to recreate accurate and exact 3D models capable of being used to find and fix problems during construction.

The findings indicate that the integration of BIM and 3D laser scanning technology has the tendency to mitigate issues such as building rework, improved project completion times, reduced project cost, enhanced interdisciplinary communication, cooperation and collaboration amongst the project duty holders, which ultimately enhances the overall efficiency of the construction project.

The acquisition of data using 3D laser scanner is usually conducted from the ground. Therefore, certain aspects of the building could potentially disturb data acquisition; for example, the gable and sections of eaves (fascia and soffit) could be left in a blind spot. Data acquisition using 3D laser scanner technology takes time, and the processing of the vast amount of data acquired is laborious, and if not carefully analysed, could result in errors in generated models. Furthermore, because this was an ongoing construction project, material stockpiling and planned construction works obstructed and delayed the seamless capture of scanned data points.

These findings highlight the significance of integrating BIM and 3D laser scanning technology in the construction process and emphasise the value of advanced data collection methods for effectively managing construction projects and streamlined workflows.

Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.

The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.

The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.

The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

This research aims to argue that manual geometric modeling is blocking the building information modeling (BIM) promotion to small-size companies. Therefore, it is necessary to study a manner of automated modeling to reduce the dependence of BIM implementation on manpower. This paper aims to make a study into such a system to propose both its theory and prototype.

This research took a prototyping as the methodology, which consists of three steps: (1) proposing a theoretical framework supporting automated geometric modeling process; (2) developing a prototype system based on the framework; (3) conducting a testing for the prototype system on its performance.

Previous researches into automated geometric modeling only respectively focused on a specific procedure for a particular engineering domain. No general model was abstracted to support generic geometric modeling. This paper, taking higher level of abstraction, proposed such a model that can describe general geometric modeling process to serve generic automated geometric modeling systems.

This paper focused on only geometric modeling, skipping non-geometric information of BIM. A complete BIM model consists of geometric and non-geometric data. Therefore, the method of combination of them is on the research agenda.

The model proposed by this paper provide a mechanism to translate engineering geometric objects into textual representations, being able to act as the kernel of generic automated geometric modeling systems, which are expected to boost BIM promotion in industry.

Nowadays, designing environmentally compatible buildings with acceptable performance in terms of cost, materials, and energy efficiency is considered crucial for developing sustainable cities. This research aims to identify and rank the most influential factors in the application of Building Information Modeling (BIM) systems in the smartification of green and sustainable buildings.

The present research is applied and descriptive. In this study, we identified the most influential factors in the application of Building Information Modeling (BIM) systems through library studies and expert opinions. Data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy VIKOR method was employed for analysis.

The results show that the most influential factors in the application of Building Information Modeling (BIM) systems in the Smartification of green and sustainable buildings, in order, are: “Energy saving and consumption reduction,” “Increased productivity and efficiency,” “Life-cycle assessment (LCA),” “Eco-friendly design,” “Integration with IoT and other technologies.”

In this study, while addressing the intersection of BIM technology, green building principles, and smart building objectives to optimize the performance of buildings during their life cycle, the most influential factors in the use of this system were ranked based on the criteria of “impact level,” “importance level,” and “availability of necessary tools” for implementation in Kerman. Moreover, solutions for more effectively utilizing this system in the smartification of green and intelligent buildings were proposed.

Building Information Modeling (BIM) capabilities have been studied at the individual, project, organizational, and even industry levels to ensure the realization of BIM value in the architectural, engineering, construction, and operation industry. However, limited research has focused on a project owner organization perspective to investigate owner BIM capabilities that are required to ensure effective project management and delivery. This present study aims to propose an indicator framework to evaluate owner BIM capabilities at the organizational level.

Leveraging the existing literature on BIM capabilities and synthesizing insights from the resource-based view and information technology capabilities research, this study conceptualizes the BIM capabilities of project owner organizations and offers a framework of indicators for measurement. Semi-structured interviews with BIM experts and a questionnaire survey were conducted to identify key indicators affecting owner BIM capabilities. A six-dimensional structural equation model with 29 indicators was then established.

The findings highlight the multidimensionality of owner BIM capabilities and show that process capabilities play a crucial role in enhancing owner BIM capabilities, while technical capabilities are considered as the least important aspect.

The study sheds light on the key role of project owner organizations in ensuring BIM value and suggests that project owners focus more on the organizational processes of introducing BIM in managing projects.

This study reconceptualizes owner BIM capabilities drawing on the idea of resource-based view and information technology capabilities and highlights the important dimensions and indicators of owner BIM capabilities at the organizational level.

The efficient application of building information modeling (BIM) methodology in the sustainable building design process, known as green BIM, provides ideal leverage to significantly enhance multidiscipline team collaboration. However, the practical execution of green BIM is characterized by issues such as duplication of work, information silos and poor cross-party coordination. Besides, there are limited studies on the specific components that are critical to driving green BIM collaborative design. This study aims to establish the critical components of green BIM collaborative design to enable the multidiscipline team to effectively use diverse software to collaboratively exchange accurate information, thus ensuring informed decision-making in the sustainable building design process.

Data were obtained by using a questionnaire to survey 360 respondents comprising mainly architects and engineers (civil, mechanical and electrical) in Malaysia. Subsequently, data were analyzed via confirmatory factor analysis. Afterward, a measurement model was established and used to test the 11 hypotheses of this study.

A covariance-based structural equation model of the critical components for successful BIM-based sustainable building design collaboration was established.

The research findings will guide the multidisciplinary team to collaboratively exchange accurate information in green BIM practices.

To the best of the authors’ knowledge, this research is the first attempt in the literature to provide a pragmatic approach for practitioners to combine the established critical components of green BIM to collaboratively exchange heterogeneous sustainability criteria and efficiently design buildings with high sustainability performance, particularly in emerging countries like Malaysia.

This paper studies the critical factors from the perspectives of technological quality, personal compatibility and organisational commitment using the technological adoption framework (TAF). The proposed TAF studies the critical factors that influence the intention to use building information modelling (BIM) taking into consideration of the “Perceived Ease of Use (PEU)” and “Perceive Usefulness (PU).”

The proposed study is a quantitative research study using the TAF model and the statistical analysis using “Partial Least Squares-Structural Equation Modelling (PLS-SEM).” The questionnaires are developed based on the literature review study and disseminated to the stakeholders in the Malaysian construction industry, including consultants, contractors, and clients. The data collected are analysed using PLS-SEM to identify the correlation between the critical factors influencing BIM adoption and the moderation influence of the PEU and PU towards the “Intention to Use (IU)” BIM.

The data collected from 185 construction industry stakeholders in Malaysia was utilised to develop the structural equation model. The measurement model was analysed in terms of composite reliability, discriminant validity, and collinearity issues. Subsequently, the SEM is analysed, and the findings on the hypothesis on the correlation between the critical factors and the intention to use BIM are examined. The study also examines the mediation effects of the PEU and PU towards the BIM adoption in the Malaysian construction industry.

This research conceptual framework, TAF, is derived from the integration of the existing underpinning theories of the technological adoption model and the technology–organisation–environment framework. This new TAF can be used for the study of new technology adoption. This cross-sectional research study is in line with the “Construction 4.0 Strategic Plan” in Malaysia to establish the current BIM adoption scenario and formulate the framework to promote incentives to promote BIM adoption.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project