Optimal Design of Continuous Reinforced Concrete Beams using Neural Networks - Yeh, Jiin-Po; Yang, Ren-Pei (Transactions on Engineering and Computing Sciences, 2015)
This paper aims to build a neural network model to optimally design two-span continuous reinforced concrete beams. The training and checking data of the neural network are obtained by genetic algorithms, whose constraints are built according to the ACI Building Code and objective function is to find the minimum cost of longitudinal reinforcement, stirrups and concrete. The neural network adopted in this paper is the feedforward backpropagation network, whose input vector consists of the span, width and effective depth of the beam, dead load, compressive strength of concrete as well as yield strength of steel and the output vector the positive and negative steel ratios and minimum total cost. The correlation coefficients between the target and network output of the testing data can reach as high as 0.9992, 0.9980 and 0.9999, respectively for the positive and negative steel ratios and minimum cost. Compared with the adaptive neuro-fuzzy inference system, the neural network shows almost the same accuracy but is much easier implemented.