Prediction on the seismic performance limits of reinforced concrete columns based on machine learning method

Abstract

The drift ratio or lateral deformation is typically applied as the indicator in order to evaluate the earthquake-induced damage, one of the most important issues is to determine the seismic performance level limits. Therefore, this study presents to predict the seismic performance level limits of RC columns by using the machine learning method. Firstly, a test database of the backbone curves of RC columns was established after collecting 754 specimens under axial and lateral loads. Then the seismic performance level limits of all the collected columns were taken out as the input values of machine learning. The correlations among the geometric, mechanical parameters and the performance limits of RC columns were analyzed based on Pearson correlation analysis and mutual information method. Afterward, regression models of seven machine learning methods were established to predict the performance level limits of RC columns, while the hyperparameters of the machine learning models were optimized by the grid search and cross-validation methods. The generalization ability of the prediction models was verified and evaluated by using mean square error, mean absolute error, maximum error and R square, meanwhile, the accuracy of the applied methods was also analyzed. The seismic performance level limits of RC columns determined by the machine learning method can comprehensively consider the influence of geometric and mechanical parameters of RC columns. Combined with the earthquake-induced deformation of RC columns, the seismic damage of RC columns can be evaluated reasonably, which is of great significance for evaluating the seismic damage of building structures. The discussion on the prediction accuracy among different machine learning algorithms is also beneficial for the deformation prediction of other RC components.