Simulation-based optimization for modeling and mitigating tunnel-induced damages

Ying Wang and Limao Zhang

Reliability Engineering and System Safety, 2021, vol. 205, issue C

Abstract: This research develops a simulation-based optimization approach that is capable of modeling and mitigating tunnel-induced damages. Two fuzzy cognitive maps (FCMs) (i.e., one with self-feedback and the other without self-feedback) are learned from historical datasets by using the real-coded genetic algorithm (RCGA) on a data-driven modeling manner. Then, the optimal variable value set is searched in the input space. Two new measures, namely “maximum response†and “average response†, are proposed to search the optimal variable value set in the input space in the FCM dynamic simulation process. A realistic tunnel case in the Wuhan metro system in China is extensively investigated to demonstrate the applicability and effectiveness of the developed approach. Results indicate that (1) The FCM with self-feedback is more stable than the FCM without self-feedback considering its higher coefficient of determination in the testing samples, where less modification of input variables realizes comparable improvement in the objective in the FCM with self-feedback. (2) The measure “maximum response†shows a larger change in the objective than the measure “average response†, where modifications in input space are similar. (3) It is revealed that the ground settlement is more sensitive to TBM operational parameters than tunnel geometry and geological conditions in the two learned FCMs. The developed approach provides insights into a better understanding of causal relationships among factors in tunnel-induced damages, enabling the planning of proactive control strategies for mitigating tunnel-induced damages.

Keywords: Simulation-based optimization; Tunnel-induced damage; FCM; Data-driven modeling; Mitigation control (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307638

DOI: 10.1016/j.ress.2020.107264

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