Journal article Open Access

Using 3D digital image correlation in an identification of defects of trees subjected to bending

Jan Tippner; Luděk Praus; Martin Brabec; Václav Sebera; Barbora Vojáčková; Jaromír Milch


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    <subfield code="a">Arboriculture</subfield>
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    <subfield code="a">Optical non-contact assessment</subfield>
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    <subfield code="a">Pulling test</subfield>
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    <subfield code="a">Using 3D digital image correlation in an identification of defects of trees subjected to bending</subfield>
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    <subfield code="a">&lt;p&gt;Abrupt changes of climate have intensified during the last few decades, bringing higher risks from tree failures by either uprooting or stem breakage. To eliminate the risks, many techniques of tree assessment are being used. In the presented work, an optical technique based on 3D Digital Image Correlation (3D-DIC) was investigated as one of the tools to be used in identification of tree defects. Within the work, two ash trees were examined by pulling tests coupling 3D-DIC and standard techniques. The trees were measured in five consecutive steps of artificially made defects of two kinds - root and stem damage. We hypothesized defects can be identified using full-field strains and displacements. Results indicated that 3D-DIC provides comparable strains as standard semidestructive extensometers. Statistical tests (&amp;alpha; = 0.05) showed the 3D-DIC technique method is capable of identifying changes of displacements and strains after creating artificial defects in trees. However, despite the statistical differences, the practical arboricultural considerations of findings are still limited due to low absolute differences. The study also suggests there might exist path-dependency of the defect creation order when evaluating stiffness/strains from extensometers of two different positions. This could have impact on a practical assessment of tree stability in the future, but it must be further tested on larger data sets due to the proof-ofconcept character of this work. In general, 3D-DIC brings extensive improvement in data acquisition quality and quantity, especially from the perspective of natural variability and heterogeneity in trees and wood.&lt;/p&gt;</subfield>
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