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Issue title: Special Section: Applied Machine Learning and Management of Volatility, Uncertainty, Complexity & Ambiguity (V.U.C.A)
Guest editors: Srikanta Patnaik
Article type: Research Article
Authors: Zhang, Yinglong | Hu, Hao*; | Xu, Feng
Affiliations: School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
Correspondence: [*] Corresponding author. Hao Hu, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, P.R.China. E-mail: [email protected].
Abstract: Construction change management is an important part of construction project management. Previous studies usually focus on the various steps of construction change control: analyzing the causes of change, avoiding the risk of change, tracking the process of change, management and feedback of change. This study focused on improving the information flow and organizational relationship of the participants in the process of construction change management, which was reengineered with the use of Building Information Modelling (BIM) technology for better information integration. As BIM technology provides technical means for information transaction and management, a reengineered construction change management process based on BIM technology was proposed to form a more effective way of putting forward, examine, issuing, updating and archiving the information. Both the workflow and the organizational units involved in each work step were reengineered to improve the information flow. To verify the effect of process reengineering, Uncinet, a visual analysis tool for Social network analysis (SNA) was applied to quantitatively analyze and compare the traditional and BIM based organizational structure. The research results demonstrated that BIM was helpful to strengthen organizational coordination and information exchange in construction change management process.
Keywords: Construction change management, process reengineering, building information modelling, visual analysis, social network analysis
DOI: 10.3233/JIFS-179921
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 1471-1480, 2020
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