Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning

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dc.contributor.authorOnyelowe, Kennedy C.
dc.contributor.authorKamchoom, Viroon
dc.contributor.authorHanandeh, Shadi
dc.contributor.authorAnandha Kumar, S.
dc.contributor.authorZabala Vizuete, Rolando Fabián
dc.contributor.authorSantillán Murillo, Rodney Orlando
dc.contributor.authorZurita Polo, Susana Monserrat
dc.contributor.authorTorres Castillo, Rolando Marcel
dc.contributor.authorEbid, Ahmed M.
dc.contributor.authorAwoyera, Paul
dc.contributor.authorArunachalam, Krishna Prakash
dc.date.accessioned2026-08-06T10:52:42Z
dc.date.available2026-08-06T10:52:42Z
dc.date.issued2025-12-01
dc.description.abstractPhysics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide. In this research work, an extensive literature review, which produced a global representative database for the splitting tensile strength (Fsp) of recycled aggregate concrete, was indulged. The studied concrete components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, and F_type were measured and tabulated. The collected 257 records were partitioned into training set of 200 records (80%) and validation set of 57 records (20%) in line with a more reliable partitioning of database. Five advanced machine learning techniques created using the “Weka Data Mining” software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. The results show the Kstar model demonstrates the highest level of performance and reliability among the models, achieving exceptional accuracy with an R<sup>2</sup> of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model’s superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix.
dc.identifier.citationScientific Reports, 15(1), 2025
dc.identifier.doi10.1038/s41598-025-91980-3
dc.identifier.issn20452322
dc.identifier.other2-s2.0-85219626250
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17373
dc.sourceScientific Reports
dc.subjectConcrete structures
dc.subjectPhysics-informed modeling
dc.subjectRecycled aggregate concrete
dc.subjectSplitting tensile strength
dc.subjectSustainable construction
dc.titlePhysics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning
dc.typeArticle

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