An effective multi-time series model of RC column backbone curve identification

Abstract

Accurate identification of the backbone curves of reinforced concrete (RC) columns is key to engineering design and strengthening renovation. In view of the problems of high cost, long time, low accuracy, large dispersion of calculation results and discontinuous stiffness changes of the existing backbone curve identification methods, such as experimental method, finite element simulation method and semi-theoretical and semi-empirical method, it is proposed to transform the backbone curve identification problem into a multi-time series prediction problem. By introducing the attention mechanism and combining it with the bidirectional long short-term memory (BiLSTM), the backbone curve identification model (BC-ABiLSTM) is established considering the relationship between the front and back points of the backbone curves. Compared with the models for backbone curves with BiLSTM (BC-BiLSTM), long short-term memory (BC-LSTM), multilayer perceptron (BC-MLP), and the existing identification methods, the performance of the BC-ABiLSTM model is better, and the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2 of the BC-ABiLSTM model on the testing set are 12.492 kN, 10.595 %, 20.838 kN and 0.9924, respectively, which provides a new method for accurate, efficient and cost-effective prediction of RC column backbone curve points under various cyclic loading levels.

Keywords

• RC column
• Backbone curve
• Multi-time series
• Bidirectional long short-term memory
• Attention mechanism