Using Neural Networks to Prediction of compressive strength of heavy concrete

East Siberia State University of Technology and Management, 40V Klyuchevskaya str., 670013 Ulan-Ude, Russia

^* Corresponding author: obeka_nlv@mail.ru

Abstract

The article is devoted to the study of the process of predicting the compressive strength of concrete. Fully connected neural networks are used as a forecasting tool. The need for research is caused by the fact that concrete is one of the materials widely used in construction, and the existing automated tools have insufficient accuracy. The paper investigates the structure of a neural network: select of the number of layers, the number of neurons in layers, the activation function, the optimization method, the number of epochs, and the technique to prevent overfitting. Comparison of the obtained results with the results of laboratory tests showed that neural networks could achieve acceptable prediction accuracy. The coefficient of determination refers to the main indicators of the quality of forecasting. Now, the coefficient of determination is approximately equal to 0.889. In the future, the started research can be continued and the value of the coefficient of determination can be improved.