Machine learning models for predicting the compressive strength of concrete containing nano silica

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation???s standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.