Cross-laminated timber (CLT) market demand is on the rise in the United States. Adequate protective measures have not been extensively studied. The objective of this study was to investigate the weathering performance of exterior wood coatings. We evaluated coated CLT sample surfaces based on visual appearance, color change (CIE*L*a*b), gloss changes, and water intrusion. From the five exterior wood coatings evaluated, only two showed adequate performance after twelve months field exposure. Based on visual ratings following the ASTM procedures, coating failure occurs more quickly in Mississippi than in Wisconsin, due to its greater decay zone. Both location and coating type impacted the aging of the samples. Artificial weathering results were consistent with natural weathering indicating the two adequate coatings were the most resistant to failure, color, and gloss change. For future studies, new coatings designed for the protection of end-grain in CLT panels should be a target of research and development.
Heat treatment (HT) of a mixture of tannins and inorganic boron compounds showed effective results against wood decay organisms. Boron compounds play a critical role in the preservation of wood against wood decay organisms. The use of tannins and HT represents a relatively new environmentally friendly approach to the wood preservation industry. The aim of this study was to investigate the effect of tannin impregnation and HT on boron depletion, and termite and fungi resistance. Southern yellow pine (SYP) and yellow-poplar (YP) cube samples were used for this study. A mixture of condensed tannins from the Quebracho tree and disodium octaborate tetrahydrate (DOT) was injected into the specimens using a vacuum/pressure cycle, and the specimens were then heat-treated under N 2 atmosphere for four hours at 190 • C to investigate both the tannin's fixative ability to reduce boron leaching and the performance of the mixture against brown and white-rot fungi and termites. Tannins restricted boron leaching in 46% and 34% for SYP and YP, respectively, and also significantly increased the resistance against white-rot fungi for YP. Tannins and HT showed unpredictably good performance against termites. Tannins may be acting to denature proteins; in that case, fungal enzymes would be inhibited. This study revealed the importance of HT on a mixture of boron and tannins to decrease boron leaching and increase the durability of preservative systems.products is limited because of boron's natural solubility in water, which leads to a rapid depletion of treated wood in outdoor exposed conditions [7][8][9][10][11][12].Research has been conducted to address boron depletion. The most common strategies for reducing boron leaching are surface and envelope treatments, wood bulking, the use of water repellant, organo-boron compounds, a combination of biocides and non-biocidal agents, metallo-borates, stabilized boro-esters, protein borates, in situ polymerization and boron silicates [13][14][15][16][17][18][19].Previous research has shown the potential of boron fixation by tannin auto condensation. The authors in [20] described a mechanism in which boric acid was partly fixed to a condensed tannin network with boron mobility being maintained. The authors in [21] leached a mixture of tannins and boron for 80 h. After the leaching period, a 30% loss of boron occurred. The trend of increased leaching continued as the experiment progressed.The effectiveness of tannins toward fixing boron under high temperatures and for longer leaching periods has not yet been investigated. In addition, little research has been done regarding boron's efficacy on multiple wood species. Broadening this spectrum is relevant in order to improve durability across the widely varying range of species and characteristics of wood [22][23][24].In this study, a solution of disodium octaborate tetrahydrate (DOT) and condensed tannins were pressure and heat-treated into southern yellow pine and yellow-poplar with the aim of reducing boron leaching. In addition, performance ...
This study reports the feasibility of using deep convolutional neural networks (CNN), for automatically detecting knots on the surface of wood with high speed and accuracy. A limited dataset of 921 images were photographed in different contexts and divided into 80:20 ratio for training and validation, respectively. The “You only look once” (YoloV3) CNN-based architecture was adopted for training the neural network. The Adam gradient descent optimizer algorithm was used to iteratively minimize the generalized intersection-over-union loss function. Knots on the surface of wood were manually annotated. Images and annotations were analyzed by a stack of convolutional and fully connected layers with skipped connections. After training, model checkpoint was created and inferences on the validation set were made. The quality of results was assessed by several metrics: precision, recall, F1-score, average precision, and precision x recall curve. Results indicated that YoloV3 provided knot detection time of approximately 0.0102 s per knot with a relatively low false positive and false negative ratios. Precision, recall, f1-score metrics reached 0.77, 0.79, and 0.78, respectively. The average precision was 80%. With an adequate number of images, it is possible to improve this tool for use within sawmills in the forms of both workstation and mobile device applications.
This manuscript reports the feasibility of a sequential convolutional neural network (CNN) machine-learning model that correctly identifies eleven (11) North American softwood species from 14x magnified macroscopic end-grain images. The convolutional network contained a large kernel size, max pooling layers, and leaky rectified linear units to accelerate training. To reduce overfitting of training data, we employed L2 regularization, custom initialization, and stratified 5-fold cross-validation techniques. The database consisted of 1,789 wood end-grain images. The training dataset consisted of 1,431 images, whereas the validation set had approximately 358 images. In both sets, the input image size was 227 pixels x 227 pixels. Data augmentation was performed on-the-fly by flipping, rotating, and zooming the images. We tested the performance of the CNN against precision, sensitivity, specificity, F1-score, and adjusted accuracy. The adjusted accuracy for the entire model was 94.0%. Confusion matrices indicated the lowest performance was in correctly classifying Ponderosa pine and Eastern spruce group with an average sensitivity of 89.0% for each. Even though high validation accuracy (>94.0%) was achieved, we concluded that a much larger dataset is needed for wood identification to obtain industrially accurate identification of softwoods, mainly due to their visual and macroscopic similarities.
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