The characterization of poplar wood assumes a strategic position to increase the competitiveness of the entire forest wood supply chain. From this aspect, the identification of wood quality represents a primary objective for researchers and private landowners. The quality of wood can be defined via traditional visual methods based on the experience of technicians or using traditional tools, such as incremental drills and sound hammers. The traditional properties of these traits, based only on visual characteristics, can outline a classification based on the macroscopic properties of wood with the aim of defining the volume of recoverable wood. However, this approach does not provide a good indicator of the physical or mechanical properties of wood. Mechanical tests of wood require the felling of trees with the correlated preparation of the specimens. A different solution to determine wood quality is based on the application of non-destructive technology (NDT). In this context, the aim of the present study was to determine the predictive accuracy of non-destructive analysis of the MOEd in standing trees and logs of a 22-year-old poplar clone and to examine the relationship with MOEs in sawn specimens. This relationship was also studied at three different stem heights. We non-destructively measured poplar trees and green logs using TreeSonic and Resonance Log Grader and compared the results with those obtained via a destructive method using a universal testing machine. The results showed that for clone I-214 poplar trees, the dynamic elastic moduli of standing trees and logs were validly correlated with the static elastic modulus. These results suggest that it is possible to evaluate the mechanical properties of poplar wood directly from standing trees using non-destructive techniques (NDT) and that this tool can be easily used to presort material in the forest.

The Predictive Accuracy of Modulus of Elasticity (MOE) in the Wood of Standing Trees and Logs

Papandrea, Salvatore F.;Cataldo, Maria F.;Bernardi, Bruno;Zimbalatti, Giuseppe;Proto, Andrea R.
2022

Abstract

The characterization of poplar wood assumes a strategic position to increase the competitiveness of the entire forest wood supply chain. From this aspect, the identification of wood quality represents a primary objective for researchers and private landowners. The quality of wood can be defined via traditional visual methods based on the experience of technicians or using traditional tools, such as incremental drills and sound hammers. The traditional properties of these traits, based only on visual characteristics, can outline a classification based on the macroscopic properties of wood with the aim of defining the volume of recoverable wood. However, this approach does not provide a good indicator of the physical or mechanical properties of wood. Mechanical tests of wood require the felling of trees with the correlated preparation of the specimens. A different solution to determine wood quality is based on the application of non-destructive technology (NDT). In this context, the aim of the present study was to determine the predictive accuracy of non-destructive analysis of the MOEd in standing trees and logs of a 22-year-old poplar clone and to examine the relationship with MOEs in sawn specimens. This relationship was also studied at three different stem heights. We non-destructively measured poplar trees and green logs using TreeSonic and Resonance Log Grader and compared the results with those obtained via a destructive method using a universal testing machine. The results showed that for clone I-214 poplar trees, the dynamic elastic moduli of standing trees and logs were validly correlated with the static elastic modulus. These results suggest that it is possible to evaluate the mechanical properties of poplar wood directly from standing trees using non-destructive techniques (NDT) and that this tool can be easily used to presort material in the forest.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/128246
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