Steel and Composite Structures

  • 제44권1호
  • /
  • Pages.49-63
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  • 2022
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Development of ensemble machine learning models for evaluating seismic demands of steel moment frames

  • Nguyen, Hoang D. (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Kim, JunHee (Department of Architecture and Architectural Engineering, Yonsei University) ;
  • Shin, Myoungsu (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST))
  • 투고 : 2021.11.15
  • 심사 : 2022.06.27
  • 발행 : 2022.07.10
⟨ 이전 논문 다음 논문 ⟩

초록

This study aims to develop ensemble machine learning (ML) models for estimating the peak floor acceleration and maximum top drift of steel moment frames. For this purpose, random forest, adaptive boosting, gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) models were considered. A total of 621 steel moment frames were analyzed under 240 ground motions using OpenSees software to generate the dataset for ML models. From the results, the GBRT and XGBoost models exhibited the highest performance for predicting peak floor acceleration and maximum top drift, respectively. The significance of each input variable on the prediction was examined using the best-performing models and Shapley additive explanations approach (SHAP). It turned out that the peak ground acceleration had the most significant impact on the peak floor acceleration prediction. Meanwhile, the spectral accelerations at 1 and 2 s had the most considerable influence on the maximum top drift prediction. Finally, a graphical user interface module was created that places a pioneering step for the application of ML to estimate the seismic demands of building structures in practical design.

키워드

과제정보

This research was supported by the Mid-Career Research Program through the National Research Foundation of Korea, funded by the Ministry of Science and ICT (Grant No. NRF-2018R1A2B6004546) and the A.I. Innovation Project Fund (Grant No. 1.210089) of UNIST (Ulsan national Institute of Science and Technology).

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