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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/51750

    Título
    Assessment of machine learning algorithm-based grading of Populus x euramericana I-214 structural sawn timber
    Autor
    Acuña Rello, LuisAutoridad UVA Orcid
    Spavento, Eleana
    Casado Sanz, María MilagrosaAutoridad UVA Orcid
    Basterra Otero, Luis AlfonsoAutoridad UVA Orcid
    López Rodríguez, GamalielAutoridad UVA Orcid
    Ramón Cueto, GemmaAutoridad UVA Orcid
    Relea Gangas, EnriqueAutoridad UVA Orcid
    Morillas Romero, Leandro
    Escolano Margarit, David
    Martínez López, Roberto DiegoAutoridad UVA
    Balmori Roiz, José AntonioAutoridad UVA Orcid
    Año del Documento
    2022
    Editorial
    Elsevier
    Documento Fuente
    Engineering Structures, 2022, vol. 254, p. 113826,
    Resumen
    The efficiency of visual grading standards applied to structural timber is often inappropriate, and timber properties are either under or over-graded. Although not included in the current UNE 56544 visual grading standard, machine learning algorithms represent a promising alternative to grade structural timber. The general aim of this research was to compare the performance of machine learning algorithms based on visual defects, non-destructive techniques and sawing systems (“cut type”) with UNE 56544:1997 visual grading in order to predict the qualifying efficiency of Populus x euramericana I-214 structural timber. Visual evaluation, ultrasound and vibrational non-destructive testing, and sawing systems register (radial, tangential and mixed) were applied to characterize 945 beams. In addition, in order to retrieve actual physical-mechanical values, density and static bending destructive testing (EN-408:2011 + A1:2012) was also carried out. Several machine learning algorithms were then used to grade the beams, and their predictive accuracy was compared with that of visual grading. To do so, three scenarios were considered: a first scenario in which only visual variables were used; a second scenario in which “cut type” variables were also included; and a third scenario in which additional non-destructive variables were considered. Results showed a poor level of performance of UNE 56544:1997, with an apparent mismatch between the strength values assigned for each visual grade (established by the EN 338 standard) and the actual values. On the opposite, all algorithms performed better than visual grading and may thus be deemed as promising timber strength grading tools.
    Palabras Clave
    Poplar
    Timber grading
    Defects
    Sawing systems
    Non-destructive testing
    Strength class
    ISSN
    0141-0296
    Revisión por pares
    SI
    DOI
    10.1016/j.engstruct.2021.113826
    Patrocinador
    Junta de Castilla y León (project VA047A08)
    Version del Editor
    https://www.sciencedirect.com/science/article/pii/S0141029621018964
    Propietario de los Derechos
    © 2022 Elsevier
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/51750
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
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    • DEP42 - Artículos de revista [291]
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    Assessment of machine learning algorithm-based grading of Populus x euramericana I-214 structural sawn timber.pdf
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    Universidad de Valladolid

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