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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.

The era of digital construction, including building information modelling (BIM), has placed a high demand for a seamless collaboration of people, technology and processes in meeting a project delivery. The project actors involved in a BIM process must ensure compliance with all regulations and contractual requirements from inception. While many studies have disclosed the various contractual implications arising from BIM implementation, little has been examined on the current state of BIM in contract administration practice, particularly in the Malaysian construction industry. This study aims to explore the current state of BIM implementation with specific reference to a design-and-build construction project.

The exploratory case study involved the construction of a public complex. Semi-structured interviews were carried out with the project actors, and document analysis was performed on the project’s contractual guidelines to derive the findings.

Findings from the thematic analysis revealed the following five sequences of activities that guided the contract administration practices, particularly during the pre-contract and post-contract stages of a BIM-based construction project: project inception, tendering phase, detailed design, construction and preparation for the handing over phase. Most of the activities were conducted to deliver the final BIM outputs successfully. Nevertheless, some shortcomings were noted in the project monitoring, the validation process of the BIM deliverables and the BIM players’ competency level in fulfilling the specified BIM contractual requirements.

The study contributes to a practical understanding of how BIM can affect project administration and how a public client can improve contractual risk allocation in future BIM implementation.

This paper discloses the current practices of contract administration to better understand the impacts of BIM management during the pre-contract and post-contract stages of a BIM-based construction project.

This study investigated the concerns and plans of construction professionals about building information modeling (BIM) implementation, found the acceptable BIM implementation driving forces and strategies for them and developed a prescriptive BIM implementation model to help understand how BIM implementation concerns, intentions, driving forces and strategies are connected.

This study employs a positivist paradigm with a hypothetico-deductive research strategy as well as concern-based adoption theory as a conceptual lens to distinguish construction professionals (CPs)' BIM implementation concerns and intentions. This implies that the forces driving BIM implementation intentions and concerns are related to BIM implementation methods and that their concentrations are proportional to the intensity of BIM implementation strategies. A 16-item questionnaire tailored to the operations of CPs was used for data collection. The data collected from respondents were utilized to evaluate the proposed model using structural equation modeling (SEM) techniques.

Findings from the data collected from the respondents revealed that CPs are concerned about the impact of BIM deployment on their time and service quality. Their main purpose was to take drives to learn more about BIM in order to pique their curiosity. Embracing the latest digital technology and beginning self-initiated BIM training are two strategies that would be quite effective in boosting BIM deployment.

The study identifies promising directions for future BIM implementation research and development. The study's findings imply that more theoretically motivated research, rather than just empirical research, is required to refine BIM implementation concerns.

The study has implications for the professional development of CPs as well as understanding the process of implementing BIM change. The study's findings will help to understand the resource system for assessing CPs' needs and concerns and selecting personalized BIM implementation strategies.

Before this study, BIM-related studies had ignored the concerns and goals of the CPs when it came to implementing BIM. Using the CPs' concerns and hopes for BIM implementation, a systemic BIM implementation model was developed that would help and speed up BIM adoption.

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.

Several studies have addressed the use of four-dimensional (4D) building information modeling (BIM) for construction management. However, the automation of the processes for generating 4D models and their integrated use with Location-Based Planning and the Last Planner® System is not well discussed. Therefore, this paper aims to develop a method for automating the generation and use of 4D BIM models integrated with Location-Based Planning and Last Planner® System supporting project control cycles.

The research strategy adopted was Design Science Research. The automated method for using the 4D models was developed and refined in two residential building projects in Brazil, along with 31 meetings and involving 11 direct users. The assessment of the proposed method focuses on four constructs: the impact of process automation, the impact on the identification and assessment of site progress and the planning process, ease of adoption and utility of the proposed method.

The results of this paper indicated increased adherence between planned and executed through an automated method for using the 4D models. The established routines enabled automating the link between the planning levels and the three-dimensional (3D) model, providing a more agile and updated data source and achieving 92.8% of user satisfaction regarding the deadline and frequency of delivery of the 4D model reports. Moreover, this study identified the relationships between the processes of the method proposed and Digital Models.

The primary scientific value achieved in this study is creating a method for automating processes and simplifying steps for the generation and use of 4D BIM models in the production planning and control cycles during the construction phase.

Some BIM implementation strategies are critical, while others are insignificant and ineffective in terms of lowering BIM adoption obstacles and promoting widespread acceptance of BIM in projects. The BIM literature has not provided evidence to support this claim or identify the fundamental BIM implementation strategies. This study filled this gap by identifying and investigating the effect of fundamental BIM implementation methodologies on the occurrence of proportional impacts between stages.

The findings indicate a positive and crucial relationship between the stages of the BIM implementation strategies and the reduction of barriers (r = 1.79, z = 2.15), preliminary and sustained BIM adoption barriers (r = 1.53, z = 60.83), BIM adoption on projects and BIM-enabled integration and collaboration (r = 0.7, z = 2.74). This validates the model's hypotheses and demonstrates that the fundamental BIM implementation strategies will accelerate the reduction of BIM adoption hurdles. The impact, however, will be the same for both sustained and preliminary barriers. This implies that lowering the obstacles alone will not secure BIM adoption.

The validation of the model's hypotheses demonstrated that the fundamental BIM implementation strategies will accelerate the reduction of BIM adoption hurdles. The impact, however, will be the same for both sustained and preliminary barriers. This implies that lowering BIM adoption obstacles alone will not secure BIM adoption. This study proposes that BIM deployment tactics be carefully chosen in order to remove preliminary impediments, reduce barriers and energise BIM acceptance.

In line with the findings of this study, BIM adoption must be consistent and long-term before the benefits in project execution become evident and substantiated. The BIM abilities of project participants, the feasibility of BIM conventions and the proficiency of BIM supervisors may all play a role in this.

This research implies a few approaches and tactics for implementing BIM in an efficient and beneficial manner. The study's findings imply that BIM deployment tactics should be carefully chosen in order to remove preliminary impediments, reduce barriers and energise BIM acceptance. It also explains how to structure BIM implementation strategies in developing countries. Another significant practical implication is the model's conclusion on the benefits of BIM adoption.

This study emphasises the significance of understanding the interrelatedness between the stages of the BIM implementation process in order to promote unavoidable and high-quality BIM adoption, as well as identifying the essential strategies that will guide policymakers and industry players in improving and pursuing successful BIM implementation. This study adds to BIM implementation theories by providing new information on the models, stages and interconnections of the BIM implementation process. However, the study could not identify the factors that impede BIM adoption despite the reduction in barriers and use of fundamental BIM implementation strategies.

Building information modelling (BIM) has transformed the traditional practices of the Architecture, Engineering and Construction (AEC) industry. BIM creates a collaborative digital representation of built environment data. Competitive advantage can be achieved with collaborative project delivery and rich information modelling. Despite the abundant benefits, BIM’s adoption in the AEC is susceptible to confrontation. A substantial impediment to BIM adoption often cited is data interoperability. Other facets of interoperability got limited attention. Other academic areas, including information systems, discuss the interoperability construct ahead of data interoperability. These interoperability factors have yet to be surveyed in the AEC industry. This study aims to investigate the effect of interoperability factors on BIM adoption and develop a comprehensive BIM adoption model.

The theoretical foundations of the proposed model are based on the European interoperability framework (EIF) and technology, organization, environment framework (TOE). Quantitative data collection from construction firms is gathered. The model has been thoroughly examined and validated using partial least squares structural equation modelling in SmartPLS software.

The study’s findings indicate that relative advantage, top management support, government support, organizational readiness and regulation support are determinants of BIM adoption. Financial constraints, complexity, lack of technical interoperability, semantic interoperability, organizational interoperability and uncertainty are barriers to BIM adoption. However, compatibility, competitive pressure and legal interoperability do not affect BIM adoption.

Finally, this study provides recommendations containing the essential technological, organizational, environmental and interoperability factors that AEC stakeholders can address to enhance BIM adoption.

To the best of the authors’ knowledge, this paper is one of the first studies to combine TOE and EIF in a single research model. This research provides empirical evidence for using the proposed model as a guide to promoting BIM adoption. As a result, the highlighted determinants can assist organizations in developing and executing successful policies that support BIM adoption in the AEC industry.