RT info:eu-repo/semantics/article
T1 Assessment of machine learning algorithm-based grading of Populus x euramericana I-214 structural sawn timber
A1 Acuña Rello, Luis
A1 Spavento, Eleana
A1 Casado Sanz, María Milagrosa
A1 Basterra Otero, Luis Alfonso
A1 López Rodríguez, Gamaliel
A1 Ramón Cueto, Gemma
A1 Relea Gangas, Enrique
A1 Morillas Romero, Leandro
A1 Escolano Margarit, David
A1 Martínez López, Roberto Diego
A1 Balmori Roiz, José Antonio
K1 Poplar
K1 Timber grading
K1 Defects
K1 Sawing systems
K1 Non-destructive testing
K1 Strength class
AB 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.
PB Elsevier
SN 0141-0296
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/51750
UL https://uvadoc.uva.es/handle/10324/51750
LA eng
NO Engineering Structures, 2022, vol. 254, p. 113826,
DS UVaDOC
RD 22-nov-2024