Zhang, Yongcheng, Xing, Xuejiao and Antwi-Afari, Maxwell Fordjour (2023). A hybrid approach for optimizing deep excavation safety measures based on Bayesian network and design structure matrix. Advanced Engineering Informatics, 58 ,
Abstract
Considering the dynamic risk factors and risk situation throughout the entire deep excavation operations, timely adjustment and optimization of safety measures can enhance the practicality of construction technical plans on sites. A digital and quantitative model representing the practical risk situation of the deep excavation is urgently required for realizing the prediction, optimization, and control of the actual construction state. Thus, this research aims to propose a real-world-oriented model integrating Bayesian network (BN) and design structure matrix (DSM) for decision-making in safety risk management. First, risk factors were identified, and the BN model was established to evaluate the anti-risk ability of the construction site. Then, a multi-objective safety measure optimization model under specific constraints was established. Particularly, the DSM was adopted to express the control relationship between risk factors and safety measures. Moreover, with genetic algorithms applied, the optimal safety measure set for on-site safety risk management can be obtained. For model validation, a deep excavation project of metro construction in Wuhan, China, was selected as a case study. The hybrid optimization model showed the characters of initiative and timeliness in construction risk management. By providing the timely and optimized combination of safety measures, the dynamic decision-making approach can proactively and effectively improve the risk resistance ability of construction sites.
Publication DOI: | https://doi.org/10.1016/j.aei.2023.102223 |
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Divisions: | College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing |
Funding Information: | This work was supported by the “ National Natural Science Foundation of China (NNSFC)” under Grant number 71901104 and Jiangsu Provincial Qinglan Project of China. Special thanks to all our participants involved in this research. |
Additional Information: | Copyright © 2023 Elsevier Ltd. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License [https://creativecommons.org/licenses/by-nc-nd/4.0/]. |
Uncontrolled Keywords: | Deep excavation,Digital twin model,Dynamic safety risk management,Multi-objective optimization,Information Systems,Artificial Intelligence |
Publication ISSN: | 1474-0346 |
Last Modified: | 09 Dec 2024 09:08 |
Date Deposited: | 20 Dec 2023 16:10 |
Full Text Link: | |
Related URLs: |
https://www.sci ... 474034623003518
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2023-10-31 |
Published Online Date: | 2023-10-19 |
Accepted Date: | 2023-10-12 |
Authors: |
Zhang, Yongcheng
Xing, Xuejiao Antwi-Afari, Maxwell Fordjour ( 0000-0002-6812-7839) |
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