An artificial neural network method for probabilistic life prediction of corroded reinforced concrete

Abstract

Corroded reinforced concrete is affected by many factors. Therefore, the effects of these uncertainties should be considered when predicting fatigue life. However, artificial neural networks cannot determine the uncertainty of fatigue life. This paper establishes a new artificial neural network method to solve this problem. First, survival rates are obtained using the minimum number of specimens method. Second, the samples are divided. Then, a decision tree is used to determine the uncertainty of the test sample using corrosion and loading conditions. Finally, an artificial neural network is used to predict the fatigue life of the test sample using the survival rate, corrosion conditions, and loading conditions. If the training samples entered the artificial neural network are small sample data, the sample data are expanded using a beta-variational autoencoder with sample division. The established method is evaluated using fatigue test data from corroded reinforced concrete. Moreover, the evaluation results show that the established method can effectively predict the survival rate and fatigue life.