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

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, specifica...

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Bibliographic Details
Published inSustainability Vol. 16; no. 15; p. 6644
Main Author Benaicha, Mouhcine
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2024
Subjects
Accuracy
Algorithms
Artificial intelligence
Building
Concrete mixing
Construction industry
Datasets
Environmental sustainability
Forecasts and trends
Neural networks
Neurons
Optimization
Quality control
Quality management
Sound waves
Velocity
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su16156644