In this study, dynamic hardness tests on solid and engineered wood flooring specimens of Eucalyptus globulus Labill. and Eucalyptus grandis W. Hill ex Maiden hardwoods were performed because nowadays, these fast-growing hardwoods are still scarcely employed for this use. Furthermore, another two examples of hardwood commonly applied on wood flooring, Quercus robur L. and Hymenaea courbaril L., were also tested. To compare their properties, a dynamic impact hardness test based on the impact of steel balls, with several diameters, and drop heights was developed. Accordingly, 120 solid wood flooring specimens and 120 engineering wood flooring specimens were producing with these four hardwood species. Dynamic impact tests were made with three steel balls of different diameters (30–40–50 mm), and they were carried out from five different drop heights (0.60–0.75–0.90–1.05–1.20 m). The impact of the steel ball drew the size of the footprint on the surface and this mark was measured with a digital caliper for both dimensions, diameter and depth, as footprint diameter (FD) and indentation depth (ID). Data from 3000 samples, corresponding to 120 different individual groups (4 species × 3 ball diameters × 5 drop height × 2 floor type) were analyzed. Results indicated that the variability of ID (CV between 19.25–25.61%) is much greater than the values achieved for FD (CV between 6.72–7.91%). Regarding the fast-growing hardwood species tested, E. globulus showed a similar behavior to traditional hardwood applied on wood flooring in Europe, Q. robur, and it could be a promising growth in the flooring industry. However, E. grandis showed the worst values compared to traditional hardwood in all test configurations.
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 nondestructive 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.
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