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

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dc.contributor.authorSaingam, Panumas
dc.contributor.authorChatveera, Burachat
dc.contributor.authorSua-Iam, Gritsada
dc.contributor.authorChaimahawan, Preeda
dc.contributor.authorSuthumma, Chisanuphong
dc.contributor.authorJoyklad, Panuwat
dc.contributor.authorHussain, Qudeer
dc.contributor.authorAhmad, Afaq
dc.date.accessioned2026-08-06T10:54:33Z
dc.date.available2026-08-06T10:54:33Z
dc.date.issued2026-02-01
dc.description.abstractThis 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.
dc.identifier.citationBuildings, 16(4), 2026
dc.identifier.doi10.3390/buildings16040851
dc.identifier.issn20755309
dc.identifier.other2-s2.0-105031530811
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17846
dc.sourceBuildings
dc.subjectanalytical modeling
dc.subjectcompressive strength
dc.subjectregression
dc.subjectsteel clamps
dc.titleFrom Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps
dc.typeArticle

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