Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162255
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dc.contributor.authorGuo, Kaien_US
dc.contributor.authorZhang, Limaoen_US
dc.date.accessioned2022-10-11T04:27:58Z-
dc.date.available2022-10-11T04:27:58Z-
dc.date.issued2022-
dc.identifier.citationGuo, K. & Zhang, L. (2022). Data-driven optimization for mitigating tunnel-induced damages. Applied Soft Computing, 115, 108128-. https://dx.doi.org/10.1016/j.asoc.2021.108128en_US
dc.identifier.issn1568-4946en_US
dc.identifier.urihttps://hdl.handle.net/10356/162255-
dc.description.abstractAlong with the rapid development of urban metro systems, the tunnel-induced damage becomes one of the most critical problems closely related to the safety of tunneling projects. It is urgent to perform an in-depth analysis, identify the key factors influencing the damage, and look for the strategies that could optimize the tunneling process to realize the tunnel-induced damage mitigation. To achieve this, a hybrid data-driven approach with the integration of random forest and non-dominant sorting genetic algorithm-II (NSGA-II) is proposed to perform the multi-objective optimization for mitigating tunnel-induced damages under uncertainty. The random forest is used to construct the meta-model between identified influential factors and objectives. NSGA-II is used to perform the optimization process based on the proposed optimization principle. A total of 16 input variables are identified, and two key factors (i.e., the accumulative settlement and building tilt rate) are determined as the optimization objectives related to the mitigation of the tunnel-induced damage. A case study is conducted to test the applicability and effectiveness of the proposed approach. Through the case study, it is found that: (1) An average damage mitigation improvement degree of 20.9% can be achieved through the proposed optimization process; (2) The optimization can gain the highest improvement degree 32.6% for the tunnel-induced damage mitigation problem when adjusting 3 influential variables; (3) The proposed approach is applicable for the damage mitigation optimization with more objectives, but the consideration of a third objective degrades the optimization improvement for the first two by 2.2% and 6.5%, respectively. The novelty lies in that: (1) The random forest algorithm is incorporated into the model to represent the complex relationship between the identified objectives and the influential factors; (2) Multi-objectives are identified for the mitigation of the tunnel-induced damages, and the optimization of the multi-objectives is realized by the integration of NSGA-II. This research enriches the area of the safety management of tunneling projects by the integration of the random forest and NSGA-II algorithms. With the proposed hybrid approach, the complex relationship between desired objectives and the influential factors could be represented, and the damage mitigation and project optimization could be realized, even potential conflict between objectives may exist.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relation04MNP002126C120en_US
dc.relation04MNP000279C120en_US
dc.relation04INS000423C120en_US
dc.relation.ispartofApplied Soft Computingen_US
dc.rights© 2021 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleData-driven optimization for mitigating tunnel-induced damagesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doi10.1016/j.asoc.2021.108128-
dc.identifier.scopus2-s2.0-85121247842-
dc.identifier.volume115en_US
dc.identifier.spage108128en_US
dc.subject.keywordsRisk Mitigationen_US
dc.subject.keywordsMulti-Objective Optimizationen_US
dc.description.acknowledgementThe Ministry of Education Tier 1 Grant, Singapore (No. 04MNP002126C120, No. 04MNP000279C120) and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120) are acknowledged for their financial support of this research.en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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