Search results

This study establishes an ontology-based framework for rework risk identification (RRI) by integrating heterogeneous data from the information flow of the prefabricated construction (PC) process. The main objective is to enhance the automation level of rework management and reduce the degree of reliance on human factors and manual operations.

The proposed framework comprises four levels aimed at managing dispersed rework risk knowledge and integrating heterogeneous data. The functionalities were realised through an integrated ontology that aligned the rework risk ontology with the PC ontology. The ontologies were developed and edited with Protégé. Ultimately, the potential benefit of the framework was validated through a case study and an expert questionnaire survey.

The framework is proven to effectively manage rework risk knowledge and can identify risk objects, clarify risk factors, determine risk events, and retrieve risk measures, thereby enabling the pre-identification of prefabricated rework risk (PRR) and improving the automation level. This study is meaningful and lays the foundation for the application of other computer methods in rework management research and practice in the future.

This research provides insights into the application of ontology to solve rework risk issues in the PC process and introduces a novel risk management method for future prefabricated project research and practice. The findings have significant theoretical value in terms of enriching the methods of risk assessment and control and the information management system of prefabricated projects.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

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.

This research investigates the capabilities of Cloud-based Building Information Modelling (CBIM) in managing quality asset information, drawing upon software engineers' perspectives. Compelling statistics highlight the relationship between building information and environmental sustainability. However, despite the growing utilisation of CBIM in the Architecture, Engineering and Construction (AEC) industry, a significant knowledge gap remains concerning its effectiveness in maintaining quality asset information.

This study employed an exploratory qualitative approach, utilising semi-structured interviews with thirteen software engineers actively developing technological solutions for the AEC industry. Following thematic analysis, the findings are categorised into four dimensions: strengths, weaknesses, opportunities and technological limitations. Subsequently, these findings are analysed in relation to previously identified information quality problems.

This research reveals that while CBIM improves project coordination and information accessibility, its effectiveness is challenged by the need for manual updates, vulnerability to human errors and dependency on network services. Technological limitations, notably the absence of automated updates for as-built drawings and the risk of data loss during file conversions in the design phase, coupled with its reduced capability to validate context-specific information from the user's viewpoint, emphasise the urgent need for managerial strategies to maximise CBIM's capabilities in addressing information quality problems.

This study augments the understanding of CBIM, highlighting the managerial implications of a robust information management process to safeguard information integrity. This approach fosters sustainable practices anchored in reliable information essential for achieving desired outcomes. The findings also have broader managerial implications, especially for sectors that employ CBIM as an instrumental tool.

The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

A lack of suitably qualified Building Information Modelling (BIM) professionals is understood to be a major barrier towards higher uptakes of BIM in the Australian construction industry. In response, Australian universities have tried to integrate the teaching of BIM into construction-related curricula, but with limited success. The acknowledged impediment is the lingering mismatch between what universities offer and what industry actually needs. However, the exact nature of that mismatch has yet to be identified. This study addresses that knowledge gap. It assesses both the current status of BIM competencies among university graduates and explores how BIM education at Australian universities may be improved to deliver BIM work readiness, as required by the industry.

This paper employed a qualitative research approach, utilizing 17 semi-structured interviews with experts in the Australian BIM industry. The Person-Organization (PO) fit theory, which emphasizes the congruence between individual and organizational characteristics, was utilized as a theoretical framework to examine the compatibility between “demand” and “ability” perspectives. The resulting data were analysed using this theoretical framework to gain insights into the PO fit perspectives in relation to BIM industry practices.

Findings reveal that graduates are generally competent regarding the use of BIM software. However, employers require much more than software skills, and expect recruits to have the capability to implement BIM as a process according to information management standards. Specifically, graduates are significantly deficient in matters of BIM protocols, collaboration and coordination, information workflows as well as completion and handover procedures.

This study is the first of its kind that bridges the gap between industry expectations and university education, in the Australian context, moving beyond the common discourse in education literature, which is exclusively focused on assessing students’ perceptions about BIM.

With the advancement of technology and more attention to environmental issues, building information modeling (BIM) and green building have become two new and growing trends in the construction industry. Therefore, this study proposes a new strategy that integrates BIM and green building rating assessments with an emphasis on Iran Green Building Rating System (IGBRS).

By creating a Revit-IGBRS project template that includes sheets related to all credits, the project compliance with the IGBRS credits and management of submittal documents for certification has been facilitated. Finally, a case study of the materials and resources category of the IGBRS system was performed to validate the BIM-IGBRS application model. All 8 criteria of this category were examined by using Dynamo programming for the Revit sample project.

A practical model for BIM and IGBRS integration is presented, which allows designers to be aware of the IGBRS scores obtained before the project’s construction phase and examine different scenarios for the highest scores. Overall, this study showed that integrating BIM and the Iranian rating system is possible with some constraints, and adding some features to BIM software can promote this integration.

Given that no study has been conducted on the integration of BIM with the Iran Green Building Rating System (IGBRS), the present research investigates utilizing building information modeling to meet the credits requirements of this rating system. The results of this research can be generalized and used in other green rating systems.

This project examines digital modeling strategies for existing buildings. In this context, it aims to question assumptions about the need for geometric accuracy and the efficacy of predefined ontologies. As a counterpoint to prevailing digital modeling strategies, this project proposes a digital modeling approach using a project-specific, emergent ontology.

Nishiki Market, in Kyoto, Japan, is studied as a test case. The emergent-ontology modeling process is introduced with an initial minimal set of operations including basic fold and trim operations applicable to surfaces. As the model develops iteratively, new situations are encountered for which existing rules are insufficient. In response, the model maker’s subjective judgment is invoked to introduce new operations, and ontological rules are allowed to expand.

The emergent-ontology approach, when executed on the Nishiki Market test case, enables representation of specific architectural qualities, highlighting semantic distinctions between digitally modeled elements of real-world features. The modeling approach generated project-specific knowledge, informing disciplinary understanding. Ontological emergence enabled semantic relationships to be disclosed and newly constructed.

The project proposes a novel methodology using an emergent ontology for digitally modeling existing buildings. Instead of remaining within the limitations a predefined ontology, the model maker’s subjective decisions shape the model’s ongoing development. This interpretive approach allows project-specific knowledge generation while challenging prevailing assumptions about accuracy and consistency in digital models of existing buildings.

This study provides a comprehensive analysis of the transition from Building Information Modelling (BIM) to digital twins (DT) in the construction industry. Specifically, the research explores the current state (themes and trends) and future directions of this emerging research domain.

A multi-stage approach was employed that combines scientometric and systematic review approaches. The scientometric analysis involves quantitative assessment of scientific publications retrieved from the Web of Science database – using software tools like VOSviewer and HistCite. The systematic review involved a rigorous synthesis and evaluation of the existing literature to identify research gaps, themes, clusters and future directions. Clusters obtained from the scientometric analysis of the co-occurrence network were then used as a subject base for a systematic study.

Emergent findings reveal a rapidly growing interest in BIM-DT integration, with over 90% of publications since 2020. The United Kingdom, China and Italy are the leading contributing countries. Five prominent research clusters identified are: (1) Construction 4.0 technologies; (2) smart cities and urban environments; (3) heritage BIM and laser scanning; (4) asset and facility management; and (5) energy and sustainability. The study highlights the potential of BIM-DT integration for enhancing project delivery, asset management and sustainability practices in the built environment. Moreover, the project’s life cycle operation phase has garnered the most attention from researchers in this field compared to other phases.

This unique study is comprehensive in its approach by combining scientometric and systematic methods to provide a quantitative and qualitative evaluation of the BIM-DT research landscape. Unlike previous reviews that focused solely on facility management, this study’s scope covers the entire construction sector. By identifying research gaps, challenges and future directions, this study establishes a solid foundation for researchers exploring this emerging field and envisions the future landscape of BIM-DT integration in the built environment.

This study aims to thoroughly explore and visualize the trends and developments of digital twin (DT) literature in the construction field while revealing future research directions for further exploration and exploitation.

The research follows a three-stage methodology. First, the bibliographic data is acquired using the Web of Science database. Second, the bibliometric methods are defined to include co-authorship analysis, citation analysis, keywords co-occurrence, thematic mapping while the software tools include MS Excel, VOSviewer and Biblioshiny. Third, analysis and findings include yearly DT publication output, influential DT publications, leading DT contributors, top DT sources and science mapping of DT literature.

This study identifies top-cited DT publications (35 out of 320) in terms of citations score, local citations score and document average citations per year. Furthermore, the key contributors with respect to authors (58 out of 1147), organizations (55 out of 427) and countries (19 out of 51) are recognized in terms of productivity, influence, activeness and scientific value. Similarly, the major publishing sources (24 out of 58) are identified using the same measures. Regarding science mapping, the DT domain comprises four research frontiers, namely, deep learning and smart city, internet of things and blockchain, DT and building information modeling and machine learning and asset management.

Through a mixed-review strategy, this study introduces a comprehensive analysis of DT literature while avoiding the subjectivity/cognitive bias of traditional review approaches. Moreover, it illuminates the promising and rising DT themes for new/seasoned researchers, institutions, editorial boards and funding agencies.