Failure-mode–independent prediction model for the peak strength of reinforced concrete columns using Bayesian neural network: A probabilistic approach

Abstract

A reasonable prediction for the peak strength of reinforced concrete (RC) columns is paramount for the seismic performance evaluation of RC structures. The available prediction models are commonly dependent on the failure mode, and each of them is only applicable to the columns with a particular one. However, the failure mode of RC columns is difficult to be identified accurately in prior, leading to the inconvenience of predicting its peak strength. To overcome this shortcoming, a probabilistic approach was proposed using Bayesian neural network (BNN) to develop a failure-mode–independent model for predicting the peak strength of RC columns directly. The results indicated that the developed model produces reasonable prediction for the peak strength of RC columns failing in different modes. For the training subset, the mean prediction accuracy of the flexure-dominated, flexure-shear-dominated, and shear-dominated columns is 0.997, 0.997, and 0.998, respectively. For the testing subset, the corresponding mean prediction accuracy is 0.957, 0.952, and 0.943. Compared to existing probabilistic models, the developed model exhibits better performance in reducing the uncertainties in peak strength prediction. Compared to existing deterministic models, the developed model could predict the peak strength of RC columns in terms of the confidence interval. In particular, if the confidence interval of peak strength is defined as the mean plus and minus two times standard deviation, 98.9% and 98.4% of the training subset and testing subset are covered. Therefore, the developed model is beneficial for engineers to address the confusion, namely, which peak prediction is the most probable one, when several deterministic models exist for a specific specimen.