Nuclear Engineering and Technology

  • 제55권8호
  • /
  • Pages.2747-2756
  • /
  • 2023
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Performance-based drift prediction of reinforced concrete shear wall using bagging ensemble method

  • Bu-Seog Ju (Department of Civil Engineering, College of Engineering, Kyung Hee University) ;
  • Shinyoung Kwag (Department of Civil and Environmental Engineering, Hanbat National University) ;
  • Sangwoo Lee (Department of Civil Engineering, College of Engineering, Kyung Hee University)
  • 투고 : 2023.03.02
  • 심사 : 2023.05.05
  • 발행 : 2023.08.25
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초록

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

키워드

과제정보

This work was supported by the Nuclear Safety Research Program through the Korea Foundation Of Nuclear Safety (KoFONS) using the financial resource granted by the Nuclear Safety and Security Commission (NSSC) of the Republic of Korea (No. 2106034). And this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT). (No.2021R1A2C1010278).

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