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Title |
Prediction of Backbone Curve Parameters of Reinforced Concrete Columns Based on the XGBoost Algorithm
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Authors |
윤준영(Jun-Young Yun) ; 조진우(Jin Woo Cho) ; 조은선(EunSeon Cho) ; 한상환(Sang Whan Han) |
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DOI |
https://doi.org/10.4334/JKCI.2025.37.5.619 |
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Keywords |
철근콘크리트 기둥; 백본커브; 기계학습; 정확도; 모델링 파라미터 reinforced concrete column; backbone curve; machine learning; accuracy; modeling parameter |
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Abstract |
When assessing the seismic performance of reinforced concrete (RC) frames, it is important to use an accurate numerical model for columns because the seismic behavior of the columns significantly affects the structural performance. In most previous studies, the parameters of column models were determined using empirical equations. However, it is difficult to fully capture the complex and nonlinear characteristics of actual columns using empirical equations developed from regression analyses. This study developed a machine learning (ML) model to construct an idealized backbone curve for RC columns. For this purpose, test data for rectangular RC columns under cyclic loading were collected from previous research. Three damage states were defined to construct the backbone curve, and the accuracy of the proposed ML model was subsequently validated. It was shown that the measured backbone curves of the collected columns could be precisely predicted using the parameter values obtained from the proposed ML model.
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