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.