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In order to improve the mechanical qualities of a concrete, various kinds of fibers are added to the concrete. In the studies, polypropylene (PP) fibers are employed as a fiber type. It has a significant place in the researches that PP fibers not only improve the mechanical qualities of the concrete under normal temperatures, but also prevents the bursting of the concrete with the internal vapour compression under high temperatures. The distributions and locations of the fibers in the concrete and the variables employed for experimental proceedings affect the mechanical results. This makes it difficult to link the obtained results to each other. In order to establish a complicated link, it is inevitable to create a learning mechanism. In this study, multilayered perceptrons (MLP) and radial basis function artificial neural network (RBFNN) models were used and their flexure strengths were sought to be predicted. Both of the neural network models put in a successful performance and enabled the prediction of the experimental results with a satisfying approximation.

Prediction of the Behaviours by the Prismatic Beams with Polypropylene Fibers under High Temperature Effects through Artificial Neural Networks