Comparative Analyses of Selected Neural Networks for Prediction of Sustainable Cementitious Composite Subsurface Tensile Strength

Abstract

The article assesses comparative analyses of some selected machine-learning algorithms for the estimation of the subsurface tensile strength of cementitious composites containing waste granite powder. Any addition of material to cementitious composites causes their properties to differ; therefore, there is always a need to prepare a precise model for estimating these properties’ values. In this research, such a model of prediction of the subsurface tensile strength has been carried out by using a hybrid approach of using a nondestructive method and neural networks. Moreover, various topologies of neural networks have been evaluated with different learning algorithms and number of hidden layers. It has been proven by the very satisfactory results of the performance parameters that such an approach might be used in practice. The errors values (MAPE, NRMSE, and MAE) of this model range from 10 to 12%, which, in the case of civil engineering practice, proves that this model is sufficient for being used. This novel approach can be a reasonable alternative for evaluating the properties of spacious cementitious composite elements where there is a need to analyse not only the compressive strength but also its subsurface tensile strength.