Structural Engineering and Mechanics

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Classification method for failure modes of RC columns based on key characteristic parameters

  • Yu, Bo (School of Civil Engineering and Architecture, Guangxi University) ;
  • Yu, Zecheng (School of Civil Engineering and Architecture, Guangxi University) ;
  • Li, Qiming (Power China Central China Electric Power Engineering Co, Ltd.) ;
  • Li, Bing (School of Civil and Environmental Engineering, Nanyang Technological University)
  • Received : 2021.11.11
  • Accepted : 2022.05.24
  • Published : 2022.10.10
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Abstract

An efficient and accurate classification method for failure modes of reinforced concrete (RC) columns was proposed based on key characteristic parameters. The weight coefficients of seven characteristic parameters for failure modes of RC columns were determined first based on the support vector machine-recursive feature elimination. Then key characteristic parameters for classifying flexure, flexure-shear and shear failure modes of RC columns were selected respectively. Subsequently, a support vector machine with key characteristic parameters (SVM-K) was proposed to classify three types of failure modes of RC columns. The optimal parameters of SVM-K were determined by using the ten-fold cross-validation and the grid-search algorithm based on 270 sets of available experimental data. Results indicate that the proposed SVM-K has high overall accuracy, recall and precision (e.g., accuracy>95%, recall>90%, precision>90%), which means that the proposed SVM-K has superior performance for classification of failure modes of RC columns. Based on the selected key characteristic parameters for different types of failure modes of RC columns, the accuracy of SVM-K is improved and the decision function of SVM-K is simplified by reducing the dimensions and number of support vectors.

Keywords

Acknowledgement

The financial support received from the National Natural Science Foundation of China (Grant Nos. 51738004, 52278162 and 62266005), the Guangxi Science Fund for Distinguished Young Scholars (2019GXNSFFA245004) and the Innovation Project of Guangxi Graduate Education (YCBZ2022011) is gratefully acknowledged.

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