Preliminary Design Of Reinforced Concrete Beams Using Neural Networks

Abstract

This paper presents a backpropagation neural network model for the preliminary design of rectangular concrete beams. The model, which is developed based on the strength design procedure of the American Concrete Institute (ACI), minimizes the beam total cost including the costs of concrete, steel, and shuttering. The backpropagation neural network was successful in accurately capturing the nonlinear characteristics of the strength design procedure. The network adequately learned a set of 375 examples during the training phase. A case study, where a set of 960 new cases were considered, was used to validate the network and to demonstrate the system's generalization and fault-tolerance properties. The network showed good generalization properties since it was able to predict the correct beam depth and steel area with a fair accuracy.