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There has been a mounting interest in building information modelling (BIM) in the construction industry sector worldwide due to its perceived benefits. However, reliance on information technology is associated with risks. The purpose of this paper is to offer a better understanding of the emerging contractual and legal risks, which might influence the successful adoption of BIM, in order to facilitate the successful implementation of BIM in the construction industry.

The risks used in the study were documented from the literature, and primary data were collected by a questionnaire survey. The analysis of the results was driven by univariate and inferential statistics (Analysis of Variance) to identify the emerging contractual and legal risks.

The findings showed that there were little significant differences in the mean rating of the occurrence of contractual and legal risks between the respondents. The study confirmed that emerging risks are likely to be related to BIM documentations, intellectual rights and liability, missing data and misplaced assumptions among project stakeholders. The results showed that BIM success depends on close collaboration, at the outset of the project, with the client, designers, contractors and consultants.

The findings suggest that contract documents and contract agreements may need to be created in accordance with the identified risks, so that the questions of contractual and legal responsibilities are appropriately defined and allocated among the participants.

Important legal and contractual risks have been identified in the application of BIM. It renders a new understanding of the risks that might influence the successful adoption of BIM.

Building information modelling (BIM) technology adoption is growing rapidly because of its perceived benefits. The purpose of this paper is to assess the perceptions of the users of BIM regarding the likelihood of managerial risks emergence that might influence the successful application of BIM, to facilitate the successful implementation of BIM in the construction industry.

Emerging managerial BIM risks were extracted from the literature. The primary data were collected via a questionnaire survey. The analysis of the results was driven by univariate and inferential statistics (analysis of variance ) to assess the emergence of managerial risks.

The study confirmed the nine most likely managerial risks that might emerge from BIM adoption, which are lack of understanding of the expectations from BIM modelling, lack of experienced and skilled personnel, lack of clarity on integration of BIM with the current business practice, conflict because of dissimilar expectations from BIM, lack of collaborative work processes and standards, lack of understanding of BIM processes, lack of understanding modelling behaviours, lack of expertise within the project team, lack of expertise within the organizations and lack of criteria for BIM project implementation.

The results will intensify the discussion about BIM risks, risk allocation and other aspects that are related to BIM methodology. The compiled list of managerial risks will help stakeholders in assessing financial implications that may result from BIM application. The list of risks could be used in pricing consultancy and construction services. More importantly, the list might be useful in developing an international standard for BIM risk management. The results showed that BIM success depends on the close collaboration, at the outset of the project, with contractors, consultants, designers and client.

Important managerial risks have been identified in the adoption of BIM. It renders a new understanding of the risks that might influence the successful application of BIM.

The overarching objective of this paper is to examine the cause–effect relationships among building and information modeling (BIM)-related risk factors and their impact on the implementation of BIM in construction projects.

This paper draws on previous research to identify and categorize BIM-related risk factors. It then develops a system dynamics (SD) model to examine the cause–effect relationships among these factors leading to issues in BIM implementation.

The proposed SD model illustrates how nine reinforcing feedback loops provide connections between four categories of risks, namely, technical, information, legal and organizational risks and, consequently, create vicious cycles that threaten the successful implementation of BIM.

This paper is among a few studies that depart from reductionism by taking a holistic approach and demonstrating how the interactions between BIM-related risk factors lead to vicious cycles that threaten the successful implementation of BIM.

Building information modelling (BIM) is not without risk, as the greater reliance on information technology has associated technical risks. Thus, the purpose of this paper is to assess the perceptions of the users of BIM regarding the likelihood of emergence of technical risks which might influence the successful application of BIM, to facilitate the successful implementation of BIM in the construction industry.

The primary data were collected via a questionnaire to document the BIM risks, where 105 responses were recorded from constructors, consultants, cost consultants and other professionals from the UK construction industry. Subsequently, the analysis of the results was driven by univariate and inferential statistics (ANOVA) to assess the perception of risk emergence.

The study found the most likely technical risks that might emerge from BIM application. These risks are complexity of transferring modelling data from one program to another, lack of understanding of the BIM for the different software platforms, interoperability shortcomings, failure to discover errors in the model and risks of different software platforms.

The results will certainly intensify the discussion about BIM risks, risk allocation and all other aspects that are related to BIM contractual processes. Also, the compiled list of risks will help stakeholders in assessing financial implications that may result from BIM application.

Important technical risks have been identified in the application of BIM. This renders a new understanding of the risks that might influence the successful application of BIM. The respondents generally agreed on the importance of the following risks: “complexity of transferring modelling data between programs from one program to another”, “lack of understanding of the BIM for the different software platforms”, “interoperability shortcomings”, “failure to discover errors in the model” and “risks of different software platforms”, which are in line with current literature.

The purpose of this paper is to identify risk factors in building information modeling (BIM) technology application, analyze the correlation between risk factors, and find the most influential risk factors using interpretative structural model (ISM), put forward a response to targeted suggestions, and provide a reference for widespread BIM technology application.

Literature reviews, ISM, a questionnaire survey and expert interviews were used in this paper. First, the risk factors of BIM technology application and their relationships were determined by consulting the relevant BIM literature. Second, to more intuitively reflect the relationships between risk factors, an ISM model based on expert interviews was established to structure the explanatory relationships and hierarchical relationships between factors. Finally, a questionnaire survey was conducted for relevant practitioners, and a scale was designed to evaluate the risk factors of BIM technology application to determine the risk factors that have a greater impact on implementing BIM technology.

The root risk factors of BIM technology consist of unclear data ownership, incomplete standard BIM systems, lack of industry insurance, lack of BIM technicians, lack of BIM project practice experience, BIM infrastructure preparation, inconsistent thinking about BIM from project participants, changes in delivery mode and software function problems.

The risk-sharing of BIM technology and the analysis of responsibilities, rights and benefits of all parties in a BIM project can be used as future research directions to solve higher management problems.

This paper describes the risks of BIM technology application for related researchers and provides another research train of thought for the follow-up studies.

The purpose of this paper is to address the current theoretical gap in integrating knowledge and experience into Building Information Model (BIM) for risk management of bridge projects by developing a tailored Risk Breakdown Structure (RBS) and formalising an active link between the resulting RBS and BIM.

A three-step approach is used in this study to develop a tailored RBS for bridge projects and a conceptual model for the linkage between the RBS and BIM. First, the integrated bridge information model is in concept separated into four levels of contents (LOCs) and six technical systems based on analysis of the Industry Foundation Classes specification, a critical review of previous studies and authors’ project experience. The second step develops a knowledge-based risk database through an extensive collection of risk data, a process of data mining, and further assessment and translation of data. A critical analysis is conducted in the last step to determine on which level the different risks should be allocated to bridge projects and to propose a conceptual model for linking the tailored RBS to the four LOCs and six technical systems of BIM.

The findings suggest that the traditional method and BIM can be merged as an integrated solution for risk management by establishing the linkage between RBS and BIM. This solution can take advantage of both the traditional method and BIM for managing risks. On the one hand, RBS enables risk information to be stored in a formal structure, used and communicated effectively. On the other hand, some features of BIM such as 3D visualisation and 4D construction scheduling can facilitate the risk identification, analysis, and communication at an early project stage.

A limitation is that RBS is a qualitative technique and only plays a limited role in quantitative risk analysis. As a result, when implementing this proposed method, further techniques may be needed for assisting quantitative risk analysis, evaluation, and treatment. Another limitation is that the proposed method has not yet been implemented for validation in practice. Hence, recommendations for future research are to: improve the quantitative risk analysis and treatment capabilities of this proposed solution; develop computer tools to support the solution; integrate the linkage into a traditional workflow; and test this solution in some small and large projects for validation.

Through linking risk information to BIM, project participants could check and review the linked information for identifying potential risks and seeking possible mitigation measures, when project information is being transferred between different people or forwarded to the next phase.

This study contributes to the theoretical development for aligning traditional methods and BIM for risk management, by introducing a new conceptual model for linking RBS to BIM.

The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants.

This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software.

The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement.

The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.

The purpose of this paper is to model the paths of risks associated with building information modeling (BIM) adoption in the Chinese architecture, engineering and construction industry.

A total of 16 risks were identified from the literature review and grouped into nine categories. The data were collected through a questionnaire survey with 95 professionals in China. The partial least square structural equation modeling was used to analyze the data.

The results suggested the risk categorization was confirmed, and that 15 hypothetical risk paths were statistically significant, which formed 13 chains of risk paths. “Inadequate relevant knowledge and expertise” was the primary root risk category of all the 13 chains of risk paths. Additionally, “technological issues,” “poor information sharing and collaboration,” and “liability for data input” had direct effect on the “cost overrun with BIM,” while all the other risks indirectly influence cost via these three risk categories.

Most of the respondents were designers because few clients and contractors have adopted BIM. In addition, the impact and likelihood of risks were accessed by respondents’ judgment based on their experience, which is a common problem of risk management research. As this study focuses on the Chinese architecture, engineering and construction industry, there would be geographical limitation on the findings.

This study provides practitioners with a clear understanding of the risks associated with BIM adoption and enables practitioners to take measures to mitigate the root risks and assure the potential benefits of BIM.

Although there have been studies on the risks associated with BIM adoption, most of them lacked empirical evidence and failed to examine the interactions between risks. This study is different from these prior studies, because it focuses on the interrelationships between risks and identifies the risk paths and root risks using the empirical data. Therefore, this study expands the literature relating to both BIM and risk management. Also, this study enables practitioners to take measures to mitigate the root risks and assure the potential benefits produced by BIM, thereby contributing to the practice.

The purpose of this study is to identify, classify and prioritize the benefits, challenges and risks for the integrated use of building information modeling (BIM), radio frequency identification (RFID) and wireless sensor network (WSN) in the architecture, engineering, construction and operation (AECO) industry.

This study relies on the mixed method approach which consists of systematic literature review, semistructured interviews and Delphi technique. A systematic literature review was performed and face-to-face semistructured interviews with seven subject matter experts (SMEs) were conducted for identification and classification purposes. Delphi method was applied in two structured rounds with eleven SMEs for prioritization purpose. These three research techniques were chosen to reach the most accurate data by combining different perspectives on the subject matter. Data gathered by these three methods was triangulated to increase the validity and reliability of this research.

Thirteen benefits, ten challenges and four risks for the integrated use of BIM, RFID and WSN were identified. The results could aid the practitioners and researchers comprehend the pros and cons of this integration by representing SMEs’ valuable insights and perspectives about the current and future status, trends, limitations and requirements of the AECO industry. The identified risks and challenges show the requirements for future studies while the benefits demonstrate the capabilities and the potential contributions of this hybrid integration to the AECO industry.

The integration of BIM, RFID and WSN is still not commonly implemented in the AECO industry. Some studies focused on this topic; however, none of them reveals the benefits, risks and challenges for integrating BIM, RFID and WSN in a holistic manner. This research makes a significant contribution to the AECO literature and industry by uncovering the benefits, challenges and risks for the integrated use of BIM, RFID and WSN that could increase industry applications.

This study aims to develop a comprehensive conceptual framework that serves as a foundation for identifying most critical delay risk drivers for Building Information Modelling (BIM)-based construction projects.

A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify key delay risk drivers in BIM-based construction projects that have significant impact on the performance of delay risk predictive modelling techniques.

The results show that contractor related driver and external related driver are the most important delay driver categories to be considered when developing delay risk predictive models for BIM-based construction projects.

This study contributes to the body of knowledge by filling the gap in lack of a conceptual framework for selecting key delay risk drivers for BIM-based construction projects, which has hampered scientific progress toward development of extremely effective delay risk predictive models for BIM-based construction projects. Furthermore, this study's analyses further confirmed a positive effect of BIM on construction project delay.