A practical approach to predict the hysteresis loop of reinforced concrete columns failing in different modes

Abstract

Accurate prediction of hysteresis loop of reinforced concrete (RC) columns in different failure modes is of utmost importance for the assessment of inelastic seismic performance of structures. In this paper, a practical approach is proposed by adopting the Bouc-Wen-Baber-Noori (BWBN) model to describe the typical hysteresis characteristics of RC columns, and applying the Artificial Neural Network (ANN) model to evaluate the BWBN model parameters. The governing equation of the BWBN model is first presented in terms of inelastic restoring force-translational displacement relationship according to the experimental data of RC columns under quasi-static cyclic testing. The structural performance database compiled by Pacific Earthquake Engineering Research (PEER) center is then adopted to determine the BWBN model parameters using the differential evolution (DE) algorithm. Furthermore, the ANN model is implemented to associate the identified BWBN model parameters with dimensionless physical parameters of RC columns failing in different modes. According to the investigation, it is found that the magnitude of BWBN model parameters is considerably affected by specific failure mode of RC columns. The accuracy of the estimated BWBN model parameters using the ANN model is examined, in which the hysteresis loop of RC columns failing in different modes can be reasonably quantified by the proposed approach.