Neural network for bending moment in continuous composite beams considering cracking and time effects in concrete

Abstract

A methodology using a neural network model has been developed for the continuous composite beams to predict the inelastic moments (typically for 20 years, considering instantaneous cracking, and time effects, i.e. creep and shrinkage, in concrete) from the elastic moments (neglecting instantaneous cracking and time effects). It is shown that the redistribution of elastic moment at a support due to instantaneous cracking along with time effects depends primarily on the instantaneous cracking at the support and adjacent supports and also that the redistribution is independent of absolute span lengths. The proposed neural network model predicts the inelastic moment ratio (ratio of elastic moment to inelastic moment) using eight input parameters. The training and testing data for the neural network is generated using a hybrid analytical–numerical method of analysis. The models have been validated for four example beams and the errors are shown to be small. The methodology enables rapid estimation of inelastic moments and requires a computational effort that is a fraction of that required for the time dependent analysis. The methodology can be extended for the composite building frames resulting in huge savings in computational time.