Machine Learning Approaches for Predicting Concrete Compressive Strength

Abstract

Concrete compressive strength (CS) plays a crucial role in infrastructure development. Accurate and timely prediction of compressive strength is crucial for optimising the performance of structural components. In this study, 776 experimental datasets were collected from past research. These datasets were analysed with different machine learning (ML) techniques. The study evaluated the applicability of ML approaches in forecasting concrete strength. The forecasted performance of the regression model was compared with different statistical parameters. In this study, output performance revealed that the random forest (RF) regression model has good CS prediction capabilities by its R-squared value of 0.91 followed by k-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DT) with 0.88, 0.84, and 0.78 respectively. Therefore, this research establishes that the ML approach has a good capacity to forecast the concrete CS based on the real database. These predictions approach plays perfect integration into the construction industry to timely prediction of CS of concrete with high precision and efficiency.