Computers and Concrete

  • 제32권3호
  • /
  • Pages.313-326
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  • 2023
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Improved ensemble machine learning framework for seismic fragility analysis of concrete shear wall system

  • Sangwoo Lee (Department of Civil Engineering, Kyung Hee University) ;
  • Shinyoung Kwag (Department of Civil and Environmental Engineering, Hanbat National University) ;
  • Bu-seog Ju (Department of Civil Engineering, Kyung Hee University)
  • 투고 : 2022.09.16
  • 심사 : 2023.05.18
  • 발행 : 2023.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

키워드

과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2022-00144328). This work was also supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1A2C1010278).

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