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Rolling shear in cross-laminated timber (CLT) has been identified as the governing factor influencing design value. Likewise, densification has been found to be an effective method of enhancing the rolling shear strength of lumber and in turn, CLT. In this study, utilizing knowledge of material properties, optimization of the compression ratio for densification has been presented. Three-layered CLT beams made from non-densified lumber, grade #1 loblolly pine (Pinus taeda L.), were subjected to a bending load at a span-to-depth ratio of eight and had a rolling shear failure at the mid-layer with a shear strength of 3 MPa. Assuming the same modulus of rupture (MOR) for both lumber and CLT made from the same species and grade, the MOR of lumber was used to calculate the minimum required shear strength (MRSS) of the transverse mid-layer to change the failure mode of the CLT beam from rolling shear to tensile failure. Using the relationship between the compression ratio and the increase in rolling shear strength, the optimized compression ratio for densification was calculated. This procedure resulted in a compression ratio of 16.67% for densification of the mid-layer to avoid rolling shear in the case of CLT beams with a span-to-depth ratio of eight. To verify this process, CLT beams with mid-layers densified at 16.67% were fabricated and submitted to a bending test. Rolling shear failure was mitigated and densified CLT beams failed in tension with a MOR similar to that of lumber, 47.45 MPa. Likewise, rolling shear strength was observed to increase by 48% for CLT that had a densified mid-layer at 16.67%.

In this study, the most effective application method of guayule resin and its effects on termite and fungal decay biological performances of wood strand-based (WSB) panels were explored. Southern yellow pine (Pinus spp. L.) wood strands were mixed with phenol formaldehyde (PF) resin to a target resin content of 5.00% and hot-pressed to manufacture the control WSB panels. For the in-situ process, a guayule resin solution was prepared and sprayed on the wood strands immediately after spraying the PF resin to a target content of 5.00%. For brushing and spraying methods, a sub-set of the control panel specimens were further brushed or sprayed with guayule resin solution on all surfaces. To understand the effects of guayule on durability, specimens cut from control and treated panels were subjected to termite resistance and fungal degradation soil block tests. The in-situ specimens with 5.00% guayule were subjected to tensile, internal bond, water absorption, and thickness swelling tests to find out whether guayule affects the mechanical performance of WSB panels. The results showed that in-situ treatment resulted in a significant reduction in the mechanical properties of wood stand-based panels. The sprayed technique resulted in more durable panels, as the mass loss was 2.21% for termites and 3.24% for fungi specimens, which decreased by 76.66% and 80.86%, respectively, when compared to the WSB controls.

This technical note determines the feasibility of using an InceptionV4_ResNetV2 convolutional neural network (CNN) to correctly identify hardwood species from macroscopic images. The method is composed of a commodity smartphone fitted with a 14× macro lens for photography. The end-grains of ten different North American hardwood species were photographed to create a dataset of 1869 images. The stratified 5-fold cross-validation machine-learning method was used, in which the number of testing samples varied from 341 to 342. Data augmentation was performed on-the-fly for each training set by rotating, zooming, and flipping images. It was found that the CNN could correctly identify hardwood species based on macroscopic images of its end-grain with an adjusted accuracy of 92.60%. With the current growing of machine-learning field, this model can then be readily deployed in a mobile application for field wood identification.