Abstract
To implement the performance-based seismic design of engineered structures, the failure modes of members must be classified. The classification method of column failure modes is analyzed using data from the Pacific Earthquake Engineering Research Center (PEER). The main factors affecting failure modes of columns include the hoop ratios, longitudinal reinforcement ratios, ratios of transverse reinforcement spacing to section depth, aspect ratios, axial compression ratios, and flexure-shear ratios. This study proposes a data-driven prediction model based on an artificial neural network (ANN) to identify the column failure modes. In this study, 111 groups of data are used, out of which 89 are used as training data and 22 are used as test data, and the ANN prediction model of failure modes is developed. The results show that the proposed method based on ANN is superior to traditional methods in identifying the column failure modes.
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Acknowledgement
This work was supported by the China Energy Engineering Group Planning & Engineering Co., Ltd. Concentrated Development Scientific Research Project (Project No. GSKJ2-T11-2019). The authors acknowledge the help by all others for this study.
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Supported by: China Energy Engineering Group Planning & Engineering Co., Ltd. Concentrated Development Scientific Research Project Under Grant No. GSKJ2-T11-2019
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Wan, H., Qi, Y., Zhao, T. et al. Prediction of column failure modes based on artificial neural network. Earthq. Eng. Eng. Vib. 22, 481–493 (2023). https://doi.org/10.1007/s11803-023-2179-7
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DOI: https://doi.org/10.1007/s11803-023-2179-7