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Advancing Masonry Engineering: Effective Prediction of Prism Strength via Machine Learning Techniques
(2026-04-01)
Saingam, Panumas
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Chatveera, Burachat
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Nawaz, Adnan
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Ali, Muhammad Hassan
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Choudhary, Sandeerah
Masonry buildings have shaped construction history since about 6500 BCE. They offer durability, strength, and cost effectiveness, especially in developing countries. Yet assessing compressive strength during construction remains challenging due to the constituent materials soil, cement, and stone, complicating standardization worldwide. In the present study, an innovative model based on a machine learning algorithm is put forth to predict the compressive strengths of prisms. Some important factors considered as input to the algorithm based on traditional methods are the brick and mortar strengths, prism geometry, mortar bed thickness, and empirically derived height-to-thickness (t) (h/t) ratios. Three different ANN algorithms are coded and trained on the input data, and they are based on the Levenberg–Marquardt algorithm, the resilient backpropagation algorithm, and the conjugate gradient algorithm. The optimal ANN model trained using the conjugate gradient Polak–Ribière algorithm (traincgp) achieves superior performance, with R2 = 0.9881, R2 = 0.9927, RMSE = 0.9914 MPa, MAE = 0.6039 MPa, MAPE = 20.9141%, VAF = 0.9881, and WI = 0.9970. Sensitivity analysis shows the height-to-thickness (h/t) ratio is the dominant influence on compressive strength, consistent with structural mechanics. The primary contributions are the systematically curated, richly parameterized dataset and its use to produce robust, physically interpretable predictions with established ANN methods.
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From Experiment to Prediction: Machine Learning Solutions for Concrete Strength Assessment with Steel Clamps
(2026-02-01)
Saingam, Panumas
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Chatveera, Burachat
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Sua-Iam, Gritsada
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Chaimahawan, Preeda
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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 R2 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 R2 value of 0.99 and the lowest RMSE, MAE, and MAPE among all models, followed closely by Gradient Boosting with an R2 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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Performance of Environmentally Friendly Concrete Containing Fly-Ash and Waste Face Mask Fibers
(2024-12-01)
Nawaz, Adnan
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Khan, Ameer Murad
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Jirasakjamroonsri, Amorntep
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Saingam, Panumas
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Ejaz, Ali
This work was carried out to explore the potential use of used face masks in concrete to develop sustainable green concrete. In this experimental study, used face masks were cut up, removing the ear stripes and internal nose steel wire, to prepare elongated fibers. These fibers were incorporated in cement fly ash mixtures as an additive to determine the response of M20-grade concrete. The Class F fly ash (FA) was employed as a fractional substitute of cement up to 25% by weight, whereas the addition of face masks occurred at 0%, 0.125%, and 0.25% by volume of concrete. The testing scheme focused on the mechanical and durability characteristics of the cement FA mixtures carried out after 3, 28, and 60 days of curing. The inclusion of FA and face mask fibers reduced the density of concrete specimens. The compressive, splitting tensile, and flexural strengths of mixes were also reduced at an early age; however, the strength characteristics improved at later ages, compared to the control mix. The combination of both materials in concrete mixtures resulted in lower water absorption, lower bulk water sorption, and lower mass loss values against acid attack at later ages. Similarly, the electrical resistance of concrete substantially enhanced by increasing the percentage of both materials. The experimental results demonstrated that processed face masks can be utilized in cement fly ash mixes without significantly compromising the resultant concrete characteristics.
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Hemp Fiber-Reinforced Polymers Composite Jacketing Technique for Sustainable and Environment-Friendly Concrete
(2024-07-01)
Saingam, Panumas
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Hussain, Qudeer
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Sua-iam, Gritsada
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Nawaz, Adnan
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Ejaz, Ali
This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRR layers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.
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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
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Bahrami, Alireza
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Saingam, Panumas
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Hussain, Qudeer
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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.

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