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

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 a...

Full description

Saved in:
Bibliographic Details
Main Authors: Wang, Ying, Zhang, Limao
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2022
Subjects:
Online Access:https://hdl.handle.net/10356/161120
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-161120
record_format dspace
spelling sg-ntu-dr.10356-1611202022-08-16T06:10:31Z Simulation-based optimization for modeling and mitigating tunnel-induced damages Wang, Ying Zhang, Limao School of Civil and Environmental Engineering Engineering::Civil engineering Simulation-Based Optimization Tunnel-Induced Damage 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. Ministry of Education (MOE) Nanyang Technological University The Ministry of Education Tier 1 Grants, Singapore (no. 04MNP000279C120 and no. 04MNP002126C120) and the Start-Up Grant at Nanyang Technological University, Singapore (no. 04INS000423C120) are acknowledged for their financial support of this research. 2022-08-16T06:10:31Z 2022-08-16T06:10:31Z 2021 Journal Article Wang, Y. & Zhang, L. (2021). Simulation-based optimization for modeling and mitigating tunnel-induced damages. Reliability Engineering and System Safety, 205, 107264-. https://dx.doi.org/10.1016/j.ress.2020.107264 0951-8320 https://hdl.handle.net/10356/161120 10.1016/j.ress.2020.107264 2-s2.0-85092924227 205 107264 en 04MNP000279C120 04MNP002126C120 04INS000423C120 Reliability Engineering and System Safety © 2020 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
Simulation-Based Optimization
Tunnel-Induced Damage
spellingShingle Engineering::Civil engineering
Simulation-Based Optimization
Tunnel-Induced Damage
Wang, Ying
Zhang, Limao
Simulation-based optimization for modeling and mitigating tunnel-induced damages
description 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.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Wang, Ying
Zhang, Limao
format Article
author Wang, Ying
Zhang, Limao
author_sort Wang, Ying
title Simulation-based optimization for modeling and mitigating tunnel-induced damages
title_short Simulation-based optimization for modeling and mitigating tunnel-induced damages
title_full Simulation-based optimization for modeling and mitigating tunnel-induced damages
title_fullStr Simulation-based optimization for modeling and mitigating tunnel-induced damages
title_full_unstemmed Simulation-based optimization for modeling and mitigating tunnel-induced damages
title_sort simulation-based optimization for modeling and mitigating tunnel-induced damages
publishDate 2022
url https://hdl.handle.net/10356/161120
_version_ 1743119557487230976