Performance-based drift prediction of reinforced concrete shear wall using bagging ensemble method

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.& COPY; 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states and help determine the repairing methods based on damage levels.