Earthquakes and Structures

  • 제23권3호
  • /
  • Pages.315-328
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  • 2022
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Machine learning tool to assess the earthquake structural safety of systems designed for wind: In application of noise barriers

  • Ali, Tabish (Department of Civil & Environmental Engineering, Hanyang University) ;
  • Lee, Jehyeong (Department of Civil & Environmental Engineering, Hanyang University) ;
  • Kim, Robin Eunju (Department of Civil & Environmental Engineering, Hanyang University)
  • 투고 : 2022.06.27
  • 심사 : 2022.10.11
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Structures designed for wind have an opposite design approach to those designed for earthquakes. These structures are usually reliable if they are constructed in an area where there is almost no or less severe earthquake. However, as seismic activity is unpredictable and it can occur anytime and anywhere, the seismic safety of structures designed for wind must be assessed. Moreover, the design approaches of wind and earthquake systems are opposite where wind design considers higher stiffness but earthquake designs demand a more flexible structure. For this reason, a novel Machine learning framework is proposed that is used to assess and classify the seismic safety of the structures designed for wind load. Moreover, suitable criteria is defined for the design of wind resistance structures considering seismic behavior. Furthermore, the structural behavior as a result of dynamic interaction between superstructure and substructure during seismic events is also studied. The proposed framework achieved an accuracy of more than 90% for classification and prediction as well, when applied to new structures and unknown ground motions.

키워드

과제정보

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22CTAP-C164093-02).

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