Peak strength prediction of reinforced concrete columns in different failure modes based on gene expression programming

Abstract

The peak strength of reinforced concrete (RC) columns plays an important role in the appraisal of inelastic seismic performance. It depends on various parameters related to the geometry, reinforcement detail, material property, confinement effect, and loading condition. In applications, it is usually a prior condition to classify the failure modes of RC columns for predicting the peak strength accurately. Yet, classifying the failure modes of RC columns in an accurate way is a difficult task due to the complexity of the shear transfer mechanism. Thus, there is a need to develop a peak strength prediction model for RC columns failing in different modes directly. In this study, an attempt has been made by implementing the gene expression programming (GEP) method to realize this purpose. The experimental data required for the implementation of the GEP method are based on extensive results of RC columns tested in quasi-static cyclic loading. To validate the efficiency of the developed model, a detailed comparison against existing equations is conducted. The comparative results indicate that the developed model produces a rational prediction for the peak strength of RC columns in various failure modes. Based on the developed model, the peak strength can be predicted in a unified way for both ductile and non-ductile RC columns, which is beneficial for the seismic evaluation of existing structures.