Abstract
A spandrel beam is a structural member lies at the edge of a frame and is connected by a joint to the floor beam extending into the slab. The spandrel beams are primarily responsible for transferring forces from a slab to the supporting edge columns. This work investigates the possibility of using the artificial neural networks to model the complicated nonlinear relationship between the various input parameters associated with reinforced concrete spandrel beams and the actual ultimate strength of them. The descent gradient backpropagation algorithm was employed for predicting the ultimate strength of the reinforced concrete spandrel beams. The optimum topology (which gives least mean square error for both training and testing with fewer number of epochs) is presented. Effects of parameters such as, number of hidden layer(s), number of nodes in the input layer, output layer and hidden layer(s), initialization weight factors and selection of the learning rate and momentum coefficient on the behaviour of the neural network have been investigated. Because of the slow convergence of results when using descent gradient backpropagation, another algorithm which is faster called "resilient backpropagation algorithm" has been used. The neural network trained with the resilient backpropagation RPROP algorithm gives better results than that trained with the steepest descent algorithm with momentum GDM algorithm.