Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques

Prediction of compressive strength and concrete composition using machine learning models is an essential feature in civil engineering applications. In the present paper, a methodology is proposed for the prediction of compressive strength of concrete and portland cement composition using three experimental data sets. Ten-fold cross-validation procedure is applied to four machine learning models Isotonic regression, Artificial neural network, Support vector machine, and Random forest. Further comparison was made with feature ranking and without feature ranking to reduce the computational time and to achieve better prediction accuracy. The accuracy of machine learning models are analyzed with four parameters, Correlation coefficient. Kendall's tau, Mean absolute error, and Root mean square error. It is observed that better prediction capability is achieved with the ten-fold cross-validation, since it gives a statistically unbiased result. Results obtain reveals a high correlation and less error between the experimental and predicted values for all the three experimental datasets consider. (C) 2019 Elsevier Ltd. All rights reserved.