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Wood Density and Moisture Content Estimation by Drilling Chips Extraction Technique
Roberto D Martínez1, José-Antonio Balmori1, Daniel F Llana2,3
1Timber Structures and Wood Technology Research Group, Universidad de Valladolid, 47014 Valladolid, Spain.
Abstract:
The novelty of this study is the development of an accurate wood moisture content (MC) estimation method based on a relatively brand-new, non-destructive testing technique (drilling chips extraction). The method is especially important in the assessment of existing timber structures, where non-destructive testing (NDT) results are affected by wood MC and should be adjusted to a reference MC, usually 12%. In the assessment of timber structures, it is not possible to determine MC by oven drying method and this should be estimated. Electrical resistance and capacitance are the conventional methods used for MC estimation. This research work aims to present an accurate MC estimation method based on the drilling chips extraction technique. For that, 99 specimens (90 × 65 × 38 mm3) from three softwood and hardwood species covering a wide range of densities (from 355 to 978 kg m-3) were tested after conditioning at five different MCs (5%, 10%, 15%, 20%, 25%). The Wood Extractor device based on the drilling chips extraction technique was used. The mass of the chips collected (drilling residue) from each drill was recorded. The results show that the MC of the chips extracted was statistically significantly different than the MC of the specimen and cannot be directly used as MC determination. However, the chips MC can be used as an estimator of specimen MC with high determination coefficients (R2 from 71% to 86%). As the main result, models to estimate density directly adjusted to a reference 12% MC from the wet and dry mass of chips extracted were developed with an R2 of 98%. In sum, the drilling chips extractor is a dependable and straightforward method to estimate MC and density from only one measurement. Density adjusted to a reference 12% MC can be directly estimated from a single model.
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