From Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps

This study investigates the confined compressive strength (Fcc) of various column geometries, specifically circular, square, and rectangular sections, under differing confinement conditions. Experimental results reveal that circular columns exhibited the highest Fcc values, significantly outperforming square and rectangular shapes. The impact of the number of clamps on Fcc is established, demonstrating that increased confinement substantially enhances compressive strength. To further analyze the relationships among structural parameters, five machine learning models—Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting—were employed to predict Fcc based on geometric configurations and confinement levels. Among these models, AdaBoost and Gradient Boosting achieved the highest predictive accuracy, with R-squared values of 0.99 and 0.98, respectively. Comprehensive data visualization techniques, including Regression Error Characteristic (REC) curves and SHAP value analysis, provided insights into model performance and feature importance. The results emphasized the critical roles of confinement and geometric parameters in determining the compressive strength of columns. This research highlights the potential of machine learning methodologies in structural engineering to optimize design processes and enhance material performance. Future work may expand on these findings, exploring additional shapes and confinement strategies to improve understanding of structural integrity under various loading scenarios.