Advancing Masonry Engineering: Effective Prediction of Prism Strength via Machine Learning Techniques

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dc.contributor.authorSaingam, Panumas
dc.contributor.authorChatveera, Burachat
dc.contributor.authorNawaz, Adnan
dc.contributor.authorAli, Muhammad Hassan
dc.contributor.authorChoudhary, Sandeerah
dc.contributor.authorSalman, Muhammad
dc.contributor.authorNoman, Muhammad
dc.contributor.authorChaimahawan, Preeda
dc.contributor.authorSuthumma, Chisanuphong
dc.contributor.authorHussain, Qudeer
dc.contributor.authorMehmood, Tahir
dc.contributor.authorSuparp, Suniti
dc.contributor.authorSua-Iam, Gritsada
dc.date.accessioned2026-08-06T10:55:06Z
dc.date.available2026-08-06T10:55:06Z
dc.date.issued2026-04-01
dc.description.abstractMasonry 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.
dc.identifier.citationBuildings, 16(8), 2026
dc.identifier.doi10.3390/buildings16081471
dc.identifier.issn20755309
dc.identifier.other2-s2.0-105036848906
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17994
dc.sourceBuildings
dc.subjectartificial neural networks
dc.subjectcompressive strength
dc.subjectmachine learning
dc.subjectmasonry prisms
dc.subjectsensitivity analysis
dc.titleAdvancing Masonry Engineering: Effective Prediction of Prism Strength via Machine Learning Techniques
dc.typeArticle

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