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has been cited by the following article:

• TITLE: Using Radial Neural Network to Predict the Ultimate Moment of a Reinforced Concrete Beam Reinforced with Composites

  AUTHORS: Santatra Mitsinjo Randrianarisoa, Lydie Chantale Andriambahoaka, Herimiah Stelarijao Rakotondranja, Andrianary Lala Raminosoa

  KEYWORDS: Nash-Sutcliffe Criteria, Ultimate Limit State, Simple Bending, BAEL, RBF Neural Network, Bayesian Regularization

  JOURNAL NAME: Open Journal of Civil Engineering, Vol.12 No.3, August 30, 2022

  ABSTRACT: This article is intended as a proposal for a numerical model for the prediction of the ultimate moment of a reinforced concrete beam reinforced with composite materials based on neural networks, which are classified in the artificial intelligence method. In this work, a RBF network or radial basis function type model was created and tested. The validation of the RBF architecture consists in judging its predictive capacity by using the weights and biases computed during the training, to apply them to another database which did not participate to the training and testing of the model. So, with Bayesian regularization, a maximum error of 0.0813 Tm in absolute value was found between the targets and predicted outputs. The value of the mean square error MSE = 1.1106 * 10-4 allowed us to quantify and justify the prediction performance of this network. Through this article, RBF network model was justified perform and can be used and exploited by our engineers with a high reliability rate.