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Author: search.filters.author.Joyklad, PanuwatSubject: search.filters.subject.analytical modeling

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    From Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps
    (2026-02-01)
    Saingam, Panumas
    ;
    Chatveera, Burachat
    ;
    Sua-Iam, Gritsada
    ;
    Chaimahawan, Preeda
    ;
    Suthumma, Chisanuphong
    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.
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    Incorporation of Steel Fibers to Enhance Performance of Sustainable Concrete Made with Waste Brick Aggregates: Experimental and Regression-Based Approaches
    (2023-11-01)
    Yooprasertchai, Ekkachai
    ;
    Bahrami, Alireza
    ;
    Saingam, Panumas
    ;
    Hussain, Qudeer
    ;
    Ejaz, Ali
    Each year, an enormous amount of construction waste is produced worldwide. The reuse of construction waste in construction works is a sustainable solution. The present research work utilized recycled brick aggregates in the production of concrete. The resulting concrete exhibited substandard splitting tensile, flexural, and compressive properties. Steel fibers were used to improve these substandard properties of recycled brick aggregate concrete. The volume fractions of 1%, 2%, and 3% for steel fibers were mixed in concrete, whereas recycled brick aggregates were obtained from solid fired-clay bricks, hollow fired-clay bricks, and cement–clay interlocking bricks. The compressive strength was enhanced by up to 35.53% and 66.67% for natural and recycled brick aggregate concrete, respectively. Strengthened flexural specimens demonstrated up to 8765.69% increase in the energy dissipation. Specimens strengthened with steel fibers showed substantially improved splitting tensile, flexural, and compressive responses. Separate equations were proposed to predict the peak compressive strength, strain at peak compressive strength, elastic modulus, and post-peak modulus of recycled brick aggregate concrete. The proposed regression equations were utilized in combination with an existing compressive stress–strain model. A close agreement was observed between experimental and predicted compressive stress–strain curves of recycled brick aggregate concrete.