Predict the Ultimate Moment Capacity of Reactive Powder Concrete Beams Exposed to Fire Flame Using Artificial Neural Network and Multiple Linear Regression Models

Abstract

Some of the authors of this publication are also working on these related projects: professor View project Numerical and experimental analysis of fire effects on ductility and stiffness of reinforced reactive powder concrete columns under axial compression View project Abstract-In this paper, the mathematical model has been used to predicting ultimate bending moment capacity of RPC and NSC beam specimens. From observed data and present experimental test results, Multi Linear Regression technique (RT) and Artificial Neural Network Multi Layers Perceptron (ANN-MLP) models are proposed for predictions. The accuracy of the proposed equations was examined by comparison with similar existing equations and available experimental results. The models are built, trained and tested using 25 data sets. The data used in the models consists of four input parameters, which are the compressive strength , volume fraction of steel fibers , concrete cover , burning temperature level .A combined experimental and modeling study was taken to develop a database of the estimation ability of the effects of exposure to real fire flame on ultimate load capacity of RPC and NSC in addition to others (independent variables) to predict the dependent variable using IBM  SPSS  Statistics version 21 program. It is shown that ANN model with three neurons in hidden layer predicts the ultimate moment capacity of reinforced concrete beams before and after exposure to fire flame with high degree of accuracy, the moment capacities predicted by ANN are in line with the results provided by the ultimate moment capacity of experimental test .