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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 R<sup>2</sup> = 0.9881, R<sup>2</sup> = 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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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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