Prediction of compressive strength of mortar near sodium chloride by artificial neural network

Abstract

The compressive strength value of cement mortar is usually affected by the amount of sodium chloride, chemical admixture and cement grade. Therefore, the artificial neural network model is used to predict the mortar strength values of different cement grades and sodium chloride (NaCl) content. In order to predict the compressive strength of the mortar, an artificial neural network model of cement grade, different water-cement ratio, sodium chloride solution content, an output neuron and four input neurons was established. After immersion in 0%, 5%, and 10% sodium trichloride solution for 60 days, the compressive strength was measured using 12 different ratios. Artificial neural network (ANN) analysis shows that ANN as a nonlinear statistical data modeling tool can establish a strong correlation between sodium chloride content and compressive strength of cement mortar. In addition, modeling tools have a greater impact on different cement grades (42.5, 32.5 MPa).