Comparative study of machine learning models for predicting the compressive strength of concrete using Non-Destructive Testing methods

Abstract

This study proposes and compares the conventional machine learning (CML) models as artificial neural networks (ANN), support vector machines (SVM) and ensemble machine learning (EML) models namely, random forest (RF), and gradient boosting (GB). These four-machine learning (ML) models were used to predict the compressive strength (CS) of concrete based on two non-destructive testing (NDT) methods, namely ultrasonic pulse velocity (UPV) and rebound hammer number (RHN). The article uses a large and reliable database of experimental data, collected from the literature to train and test the models, and evaluates their performance using various metrics, scatter plots and a Taylor diagram. It was found that gradient boosting (GB) is the most accurate and robust model for this study, followed by RF model, while ANN and SVM have lower performance. For this study, it suggests that EML models are more suitable and reliable than CML models for predicting the CS of concrete based on NDT methods.