Compressive Strength Prediction of High-Strength Concrete Using Regression and ANN Models

Abstract

High-strength concrete (HSC) is one of the most popular terminologies used in the concrete technology, which is known for benefits like high workable, durable and high ultimate strength. The estimation of the compressive strength (CS) using experimental method is too expensive and time-consuming procedure and small error will lead to repetition of the work, and to overcome this, alternative methods are used for prediction of the CS of HSC. In the present study, the experimentally investigated HSC data pertaining to various mix proportions are collected from authenticated journal papers, which are used to predict the CS using regression analysis—multilinear regression (MLR) and artificial neural network (ANN) models. The collected data set is divided into two groups, one for training and other for testing. The input parameters used in regression and ANN models are cement content, super plasticizer, coarse aggregate, fly ash, fine aggregate, silica fume, blast furnace slag, water–cement ratio and the CS of HSC at 28 days is the output parameter. The models are developed using training data set and the developed model is validated using testing data set. The comparison is made between the CS obtained from the MLR and ANN models. The ANN model yields better correlation between predicted and actual values of the CS (test correlation for MLR—45.48% and ANN—95.03%) and the percentage of error also reduces as compared to that of MLR. From this investigation, it is observed that the ANN model can be used to predict the CS of HSC.