Given the compressive strength of concrete, yield strength of steel, span, dead and live loads, singly reinforced concrete simple beams are first optimally designed using genetic algorithms with constraints satisfying the specifications of the ACI code. The objective function is to minimize the total cost of tension steels, stirrups and concrete. A variety of beams are designed for the use of the neural network. To train and test the effectiveness of the neural network, these optimal results are randomly divided into three sets: the training set, validation set and test set. This paper uses a two-layer feed forward neural network: one hidden layer and one output layer. The transfer functions for the hidden layer and output layer are tan-sigmoid and linear functions, respectively. The inputs of the neural network are the compressive strength of concrete, yield strength of steel and span, width and effective depth of the beam, as well as vertical loads the beam is subjected to; the targets of the neural network are the steel ratio and cost of the beam. To evaluate the accuracy, the regression analysis of the target and network output is carried out. Numerical results show good performance of the neural network, which can be used as a model to predict the lowest cost and steel ratio of singly reinforced concrete simple beams.
Key words: Reinforced concrete beams, genetic algorithm, neural network, regression analysis.
Copyright © 2023 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0