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Tunneling Risk Visualization Using BIM and Dynamic Bayesian Network
Ground tunneling construction increases the uncertainty of project risk, which cannot be proactively managed. Therefore, dynamic risk prediction and management become extremely significant for tunneling projects. This study proposes to combine the probability analysis of Dynamic Bayesian Network (DBN) with building information modeling (BIM) to manage risk during tunneling. Firstly, the tunnel BIM model is created based on the actual survey data. Secondly, the parameter related to risk factors stored in BIM is extracted into the DBN for risk prediction, including accurate geological information and relevant data of tunnel segments. Then the DBN is used to calculate the risk levels of different factors and to update the risk probability. The calculated risk level can be visualized in the BIM model through Dynamo, and different pipe sections are colored according to their predicted risk levels. An illustrative example is used to illustrate this method. Through the risk prediction analysis of the case, the feasibility of the method proposed in this paper is verified.
Tunneling Risk Visualization Using BIM and Dynamic Bayesian Network
Ground tunneling construction increases the uncertainty of project risk, which cannot be proactively managed. Therefore, dynamic risk prediction and management become extremely significant for tunneling projects. This study proposes to combine the probability analysis of Dynamic Bayesian Network (DBN) with building information modeling (BIM) to manage risk during tunneling. Firstly, the tunnel BIM model is created based on the actual survey data. Secondly, the parameter related to risk factors stored in BIM is extracted into the DBN for risk prediction, including accurate geological information and relevant data of tunnel segments. Then the DBN is used to calculate the risk levels of different factors and to update the risk probability. The calculated risk level can be visualized in the BIM model through Dynamo, and different pipe sections are colored according to their predicted risk levels. An illustrative example is used to illustrate this method. Through the risk prediction analysis of the case, the feasibility of the method proposed in this paper is verified.
Tunneling Risk Visualization Using BIM and Dynamic Bayesian Network
Lecture Notes in Operations res.
Guo, Hongling (editor) / Fang, Dongping (editor) / Lu, Weisheng (editor) / Peng, Yi (editor) / Deng, Ting (author) / Tang, DongDong (author) / Jin, Shuaishuai (author) / Tan, Yi (author)
International Symposium on Advancement of Construction Management and Real Estate ; 2021 ; Beijing, China
2022-09-02
12 pages
Article/Chapter (Book)
Electronic Resource
English
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