Framework for the development of artificial neural networks for predicting the load carrying capacity of RC members

This paper aims at establishing a framework for the development of artificial neural networks (ANNs) capable of realistically predicting the load-carrying capacity of reinforced concrete (RC) members. Multilayer back propagation neural networks are developed through the use of MATLAB and enriched databases which contain information describing the variation of load-carrying capacity in relation to key design parameters associated with the RC specimens (i.e. beams) considered. This work forms the basis for the development of a knowledge-based structural analysis tool capable of predicting RC structural response. A detailed discussion is provided on the different aspects of the proposed framework which include (1) the formation and analysis of the relevant (experimental) data, (2) the architecture of the ANNs, (3) the training/calibration process they undergo and finally, (4) ways of extending their applicability enabling them to predict the behaviour of RC structural forms with design parameters not represented in the available experimental database. Non-linear finite element analysis is used for validating the predictions of the ANN models developed. The comparative study reveals that the ANN models developed through the proposed framework are capable of effectively predicting the load-carrying capacity s of the RC structural forms considered quickly, accurately and without requiring significant computational resources.