Inversing Reinforced Concrete Beams Flexural Load Rating Using ANN and GA Hybrid Algorithm

Abstract

This paper presents a new method of using ANN (Artificial Neural Networks) to solve the inverse problem: predicting reinforced concrete (RC) beams flexural load rating (ratio of flexural load to ultimate bearing capacity) given apparent damage parameter (crack width, crack height, deflection). A hybrid algorithm (G-Prop) that combines a genetic algorithm (GA) and BP (back-propagation) is used to train ANN with a single hidden layer. The GA selects the initial weights and changes the number of neurons in the hidden layer through the application of specific genetic operators. The case study shows that ANN based on GA and BP is a powerful instrument for predicting flexural load rating and further for evaluating RC beams safety.