Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

Abstract

Many studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete. And there is no study for prediction of bending strength of self-leveling glass fiber reinforced concrete from mixture ingredients and slump values. In the present study, relationships between the bending strength and the mixture proportions are explored. An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results. Effect of each parameter on the bending strength is investigated with the developed model. An empirical and user-friendly formula was obtained with the generalization capabilities of the ANN. Results showed that the prediction results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural design for real-life self-leveling glass fiber reinforced concrete applications.

Prediction of Basalt Fiber Reinforced Concrete Pavement Bending Strength Values

“…Artificial neural networks (ANNs) were derived from the brain functions and human nervous systems [15,16]. In civil engineering problems, ANN systems have been widely used for prediction of fiber reinforced concrete mechanical properties with the high accuracy [17,18]. The accuracy of model can be enhanced by the improvement in hidden layer and its neurons in back propagation network [19].…”