AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements

Abstract

This study investigates the application of artificial intelligence (AI) to predict the compressive strength of self-compacting concrete (SCC) through ultrasonic measurements, thereby contributing to sustainable construction practices. By leveraging advancements in computational techniques, specifically artificial neural networks (ANNs), we developed highly accurate predictive models to forecast the compressive strength of SCC based on ultrasonic pulse velocity (UPV) measurements. Our findings demonstrate a clear correlation between higher UPV readings and improved concrete quality, despite the general trend of decreased compressive strength with increased air-entraining admixture (AEA) concentrations. The ANN models show exceptional effectiveness in predicting compressive strength, with a correlation coefficient (R2) of 0.99 between predicted and actual values, providing a robust tool for optimizing SCC mix designs and ensuring quality control. This AI-driven approach enhances sustainability by improving material efficiency and significantly reducing the need for traditional destructive testing methods, thus offering a rapid, reliable, and non-destructive alternative for assessing concrete properties.