Quantification Method of Damage States for Reinforced Concrete Columns Based on Energy Equivalence

Abstract:

Reinforced concrete （RC） columns have different failure modes under earthquake excitations. The damage state quantification methods of RC columns under different failure modes have problems including inconsistent quantification procedure， and poor prediction accuracy. Therefore， an energy equivalence principle was proposed in this study to quantify the damage states of RC columns under different failure modes using quasi-static experimental data of 246 RC columns with rectangular cross-sections. The Bayesian neural network was introduced to develop the drift angle prediction models for RC columns independent of failure patterns. The research results indicated that the energy equivalence principle can transfer the backbone curves of RC columns into an idealized elastic-plastic model. This not only facilitates the definition of yield point， peak point and ultimate point， but also benefits to compare the seismic performance of RC columns under different failure modes in a unified framework. The damage states of RC columns under different failure modes can be categorized into four groups， namely basic intact damage stage， slight damage state， moderate damage state and sever damage state using the drift angle at yield point， peak point and ultimate point. The Bayesian neural network model can predict well the drift angles of RC columns independent of failure modes at yield point， peak point and ultimate point. The traditional empirical factor method by reducing the peak strength tends to underestimate the drift angle at yield point， and overestimate the drift angle at ultimate point.