Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 전종수 | - |
dc.date.accessioned | 2022-09-23T07:15:05Z | - |
dc.date.available | 2022-09-23T07:15:05Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.citation | JOURNAL OF STRUCTURAL ENGINEERING, v. 146, no. 12, article no. 04020269 page. 1-11 | en_US |
dc.identifier.issn | 0733-9445; 1943-541X | en_US |
dc.identifier.uri | https://ascelibrary.org/doi/10.1061/%28ASCE%29ST.1943-541X.0002831 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/173806 | - |
dc.description.abstract | Regional seismic risk assessment involves many infrastructure systems, and it is computationally intensive to conduct an indi-vidual simulation of each system. This paper suggests an approach using active learning to select informative samples that help build machine learning models with fewer samples for regional damage assessment. The potential of the approach is demonstrated with (1) failure mode prediction of bridge columns, and (2) regional damage assessment of the California two-span bridge inventory with seat abutments. The active learning approach involves the selection of column attributes or bridge models that are more informative to the creation of machine learning based decision boundaries. The results reveal that an active learning target model based on 100 bridge samples can achieve a level of accuracy of 80%, which is equivalent to a machine learning model based on 480 bridge samples in the case of damage prediction following an earthquake. With the proposed approach, the computational complexity associated with regional risk assessment of bridge systems with specific attributes can be drastically reduced. The proposed approach also will help plan experimental studies that are more informative for damage assessment. DOI: 10.1061/(ASCE)ST.1943-541X.0002831. (c) 2020 American Society of Civil Engineers. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A4A1018826). | en_US |
dc.language.iso | en | en_US |
dc.publisher | ASCE-AMER SOC CIVIL ENGINEERS | en_US |
dc.subject | Machine learning; Active learning; Column failure mode; Bridge tagging; Regional seismic risk | en_US |
dc.title | Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: Active Learning Approach | en_US |
dc.type | Article | en_US |
dc.relation.no | 12 | - |
dc.relation.volume | 146 | - |
dc.identifier.doi | 10.1061/(ASCE)ST.1943-541X.0002831 | en_US |
dc.relation.page | 1-11 | - |
dc.relation.journal | JOURNAL OF STRUCTURAL ENGINEERING | - |
dc.contributor.googleauthor | Mangalathu, Sujith | - |
dc.contributor.googleauthor | Jeon, Jong-Su | - |
dc.relation.code | 2020046242 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING | - |
dc.identifier.pid | jongsujeon | - |
dc.identifier.orcid | https://orcid.org/0000-0001-6657-7265 | - |
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