An assessment of in-field non-destructive testing methods for detection of internal defects in standing live trees
AffiliationUniv Arizona, Dept Civil Engn & Engn Mech
Pruned and Unpruned log
MetadataShow full item record
PublisherSPIE-INT SOC OPTICAL ENGINEERING
CitationMohammad Sadegh Taskhiri, Mohammad Hadi Hafezi, Damien Holloway, and Paul Turner "An assessment of in-field non-destructive testing methods for detection of internal defects in standing live trees", Proc. SPIE 10972, Health Monitoring of Structural and Biological Systems XIII, 109721F (1 April 2019); https://doi.org/10.1117/12.2514270
Rights© 2019 SPIE.
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AbstractHarvesting trees that contain internal defects such as knots and cracks are neither financially nor environmentally sustainable. In hardwood plantations, it is impossible to produce sawlogs from knotty or cracked timber. The challenge is to identify internal defects in a timely and cost-effective manner prior to harvesting. The aim of this paper is to investigate non-destructive testing (NDT) methods to rapidly detect the presence of internal defects in standing live trees in plantation plots. The study highlights that whilst several methods exist, few have been actively applied in-field harvesting operations to optimise log handling and to increase transportation efficiencies. Key constraints are portability of the NDT equipment for use in-field, speed versus accuracy of measurements undertaken and the usability of different evaluation approaches for decision-support. In this paper, the field assessment involved using two non-destructive techniques, ground penetrating radar (GPR) and ultrasonics that use electromagnetic and ultrasonic sound waves respectively to penetrate the internal structure of standing trees. These assessment techniques can assist forest growers to more accurately evaluate the quality of growing stems in the field. They also open the opportunity to investigate differences across a wide selection of growing conditions and forest types to generate data that may support the generation of a software algorithm for predictive imputation of likely internal defect rates within particular forests and under particular growing conditions. The plan being to integrate this predictive imputation software into existing geographical information systems owned by industry partners to enable accurate mapping of land areas where high ratios of defects are likely to be detected to further optimise infield harvesting.
VersionFinal published version
SponsorsAustralian Research Council Industrial Transformation Training Hub 'The Centre for Forest Value'Australian Research Council