Publication: From Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps
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Issued Date
2026-02-01
Resource Type
eISSN
20755309
Scopus ID
2-s2.0-105031530811
Journal Title
Buildings
Volume
16
Issue
4
Rights Holder(s)
SCOPUS
Bibliographic Citation
Buildings Vol.16 No.4 (2026)
Suggested Citation
Saingam P., Chatveera B., Sua-Iam G., Chaimahawan P., Suthumma C., Joyklad P., Hussain Q., Ahmad A. From Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps. Buildings Vol.16 No.4 (2026). doi:10.3390/buildings16040851 Retrieved from: https://hdl.handle.net/20.500.14740/55308
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Abstract
This study examines the confined compressive strength (Fcc) of circular, square, and rectangular column geometries under varying confinement conditions. Results indicate that circular columns have the highest Fcc values, exceeding those of square and rectangular shapes. Increased confinement through clamps significantly enhances compressive strength. Five machine learning models, Linear Regression, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting, were used to predict Fcc based on geometric and confinement parameters. Linear Regression and Decision Tree models achieved moderate predictive performance, with R<sup>2</sup> values of 0.84 and 0.83, respectively, and relatively higher error measures (RMSE, MAE, and MAPE), indicating limited ability to capture complex nonlinear relationships in the data. In contrast, ensemble-based methods demonstrated superior performance. The Random Forest model improved the coefficient of determination to 0.90 while substantially reducing all error metrics, reflecting enhanced generalization through bagging. The boosting-based approaches yielded the best results, with AdaBoost achieving the highest R<sup>2</sup> value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R<sup>2</sup> of 0.98. These results confirm that ensemble learning techniques, particularly boosting algorithms, yield more accurate and robust predictions than single learners for the problem studied. Data visualization techniques, including Regression Error Characteristic curves (REC) and SHapley Additive exPlanations (SHAP) value analysis, highlighted model performance and feature importance, emphasizing the roles of confinement and geometry in compressive strength. This research demonstrates the potential of machine learning to optimize structural engineering design and suggests further exploration of alternative shapes and confinement strategies to enhance structural integrity.