Prediction of concrete compressive strength employing machine learning techniques

Abstract

Machine learning (ML) methods have proven to be reliable techniques in estimating, classifying, and predicting material strength based on varying material properties. It is observed that the nature of the problem and the data at hand governs the selection of an appropriate ML method. Therefore, to determine the accuracy of ML models to predict concrete compressive strength, fifteen different ML algorithms were applied to a given concrete compressive strength dataset. Among all the ML algorithms used, the prediction accuracy of the Support Vector Regressor (SVR) was the highest (88.18%) due to its effective performance when dealing with continuous target variables and nonlinear relationships among the features and the target.