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Integrating AI and sustainable materials: machine learning approaches to wood structural behavior

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dc.contributor.authorGonzález-Palacio, M.
dc.contributor.authorGarcía-Giraldo, JM.
dc.contributor.authorGonzález-Palacio, L.
dc.date.accessioned2025-08-15T07:55:42Z
dc.date.available2025-08-15T07:55:42Z
dc.date.issued2025
dc.descriptionReceived: February 15th, 2025 ; Accepted: July 12th, 2025 ; Published: July 25th, 2025 ; Correspondence: magonzalez@udemedellin.edu.coeng
dc.description.abstractWood is a potential construction material that provides a renewable source for this crucial task compared to other classical materials, such as steel or concrete, with high carbon fingerprinting levels. This suitable material minimizes energy use and adds more sustainability to ecological consciousness. Tree planting promotes the balance of the carbon dioxide ecosystem and captures and stores greenhouse gas emissions. Wood also has peculiar characteristics in terms of its structural strength and thermal insulation, optimizing energy consumption by reducing the need for cooling or heating needs. To use this material in construction, it is mandatory to study the resistance parameters like compressive, tensile, and shear strengths, enabling it for great-span structural projects. The traditional modeling strategies used for characterizing stress-strain performances usually simplify the assumptions, overpassing the complex mechanical behavior of the wood under different physical conditions. Nonetheless, previous analyses have shown that the traditional models may exhibit significant deviations from the actual resistance parameters since they can be limited in predicting non-linear and anisotropic properties inherent in wood. To address these limitations, this study proposes using machine-learning-based regressors to predict the mechanical properties of wood. Notably, we propose Multiple Linear Regression models to preserve the model's interpretability while preserving the ability to model the linear properties in the studied scenarios. Furthermore, we use metaheuristic models based on deep learning and ensemble methods to increase the goodness of fit of the predictions. We used an experimental campaign with a widespread type of wood characterization of different parameters under tension parallel to the grain, compression parallel and perpendicular to the grain, and shear conditions. The results showed a lower root mean square error (RMSE) and a higher determination index (R2). Preliminary results demonstrated the ability of machine-learning-based modeling to obtain more accurate and reliable mechanical behavior of renewable construction materials like wood.eng
dc.identifier.citationGonzález-Palacio, M., García-Giraldo, J. M., & González-Palacio, L. (2025). Integrating AI and sustainable materials: machine learning approaches to wood structural behavior. https://doi.org/10.15159/AR.25.073en
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2025, vol. 23, no. 3, pp. 1492–1512eng
dc.identifier.urihttp://hdl.handle.net/10492/10106
dc.identifier.urihttps://doi.org/10.15159/ar.25.073
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)eng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectartificial neural networkseng
dc.subjectmachine learningeng
dc.subjectrandom foresteng
dc.subjectsustainable materialseng
dc.subjectarticleseng
dc.titleIntegrating AI and sustainable materials: machine learning approaches to wood structural behavioreng
dc.typeArticleeng

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2025, Vol. 23, No. 3