Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design |
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| Affiliation: | 1. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037, Luoyu Road, Hongshan District, Wuhan, Hubei 430074, China;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore;3. Department of Mechanical and Electromechanical Engineering, National ILan University, ILan 26041, Taiwan;4. Huazhong University of Science and Technology – Wuxi Research Institute, Wuxi 214000, China;5. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430072, China;1. School of Economics and Management, Beihang University, Beijing 100191, China;2. Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing 100191, China;3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;1. Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China;2. Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China |
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| Abstract: | As a newly-developed information exchange and management platform, Building Information Modeling (BIM) is altering the way of collaboration among multi-engineers for civil engineering projects. During the BIM implementation, a large number of event logs are automatically generated and accumulated to record details of the model evolution. For knowledge discovery from huge logs, a novel BIM event log mining approach based on the dynamic social network analysis is presented to examine designers’ performance objectively, which has been verified in BIM event logs about an ongoing year-long design project. Relying on meaningful information extracted from time-stamped logs, networks on the monthly interval are built to graphically represent information and knowledge sharing among designers. Special emphasis is put on measuring designers’ influence by a defined new metric called “impact score”, which combines the k-shell method and 1-step neighbors to achieve comparatively low computational cost and high accurate ranking. Besides, an emerging machine learning algorithm named CatBoost is utilized to predict designers’ influence intelligently by learning features from both network structure and human behavior. It has been found that twelve networks can be easily distinguished into two collaborative patterns, whose characteristics in both network structures and designers’ behaviors are significantly different. The most influential designers are similar within the same group but varied from different groups. Extensive analytical results confirm that the method can potentially serve as month-by-month feedback to monitor the complex modeling process, which further supports managers to realize data-driven decision making for better leadership and work plan towards an optimized collaborative design. |
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| Keywords: | Dynamic social network analysis (SNA) Building Information Modeling (BIM) Collaborative pattern discovery Node influence measurement Human behavior evaluation |
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