Exploring the driving mechanism and path of BIM for green buildings
Abstract
Despite green building and BIM technology being hot spots in the construction industry, most research remains at the technical level. Leading to exploring the fundamental driving reason and mechanism of BIM for green buildings is still lacking. This paper explored BIM’s impact mechanism and driving path on green buildings from the management’s perspective to fill this gap. Based on a literature review, 18 expert interviews, and three case studies of green buildings, the influence mechanism was analysed via a qualitative method (ISM). Then, the importance of driving factors was evaluated via quantitative analysis (ANP). Specifically, this study probed the driving path by combining qualitative and quantitative analysis (ISM-ANP). The research findings show that the driving force of BIM for green buildings comes from the fundamental factor layer and is transferred to the intermediate and direct factors layer. The critical driving path of BIM for green building is to promote the visualization of building information, collaborative management, and expand real estate investment through the guidance of policies and standards. Based on research results, this paper puts forward five suggestions: 1) Improving the policy and standard system; 2) Striving to research native software; 3) Adopting an informatization project management mode; 4) Accelerating the construction and improvement of the green building industry chain; 5) Promoting government enterprise cooperation. These results may benefit not only the coupling and coordination of the two but also the construction industry’s green transformation and high-quality development.
Keyword : green building, BIM, driving mechanism, driving path, ISM, ANP
How to Cite
Yang, Y., Zhao, B., & Liu, Q. (2024). Exploring the driving mechanism and path of BIM for green buildings. Journal of Civil Engineering and Management, 30(1), 67–84. https://doi.org/10.3846/jcem.2024.20826
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Gerrish, T., Ruikar, K., Cook, M., Johnson, M., Phillip, M., & Lowry, C. (2017). BIM application to building energy performance visualisation and management: Challenges and potential. Energy and Buildings, 144, 218–228. https://doi.org/10.1016/j.enbuild.2017.03.032
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Hwang, B. G., & Tan, J. S. (2012). Green building project management: obstacles and solutions for sustainable development. Sustainable Development, 20(5), 335–349. https://doi.org/10.1002/sd.492
Jiang, S. H., Wang, N., & Wu, J. (2018). Combining BIM and ontology to facilitate intelligent green building evaluation. Journal of Computing in Civil Engineering, 32(5), Article 04018039. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000786
Kendall, J. (1999). Axial coding and the grounded theory controversy. Western Journal of Nursing Research, 21(6), 743–757. https://doi.org/10.1177/019394599902100603
Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. (2022). Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption – A systematic review. Engineering Applications of Artificial Intelligence, 115, Article 105287. https://doi.org/10.1016/j.engappai.2022.105287
Kheybari, S., Rezaie, F. M., & Farazmand, H. (2020). Analytic network process: An overview of applications. Applied Mathematics and Computation, 367, Article 124780. https://doi.org/10.1016/j.amc.2019.124780
Kreiner, H., Passer, A., & Wallbaum, H. (2015). A new systemic approach to improve the sustainability performance of office buildings in the early design stage. Energy and Buildings, 109, 385–396. https://doi.org/10.1016/j.enbuild.2015.09.040
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Kumar, S., Raut, R. D., Nayal, K., Kraus, S., Yadav, V. S., & Narkhede, B. E. (2021). To identify industry 4.0 and circular economy adoption barriers in the agriculture supply chain by using ISM-ANP. Journal of Cleaner Production, 293, Article 126023. https://doi.org/10.1016/j.jclepro.2021.126023
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