This study aimed to collect fundamental data to understand the strength characteristics of aged timber and to explore methods for estimating its mechanical properties. This study was conducted using removed timber approximately 500 years old from Josho-ji Temple in Onomichi City, Hiroshima Prefecture. The subjects of the study were Japanese red pine (Pinus densiflora) and Japanese cypress (Chamaecyparis obtusa). Dynamic Young's modulus measurements and strength tests on full-size timber and defect-free small specimens were performed. A comparison of the bending and longitudinal compression strength of full-size timber with reference values for each mechanical grading category showed that a higher Young's modulus increased the proportion of specimens falling below the reference strength. Positive correlations were observed between the dynamic Young's modulus and static Young's modulus, as well as between the dynamic/static Young's modulus and bending strength, suggesting that Young's modulus measurements are effective for estimating bending strength. For the defect-free small specimen tests, the bending strength of the aged Japanese red pine was 39.2% lower than that of new Japanese red pine, while the longitudinal compression strength showed no significant difference. The limited number of specimens makes the results for aged Japanese cypress inconclusive; however, its bending strength was 29.5% higher and longitudinal compression strength 53.5% higher than in previous studies on Japanese cypress timber. The high strength of aged Japanese cypress may be attributed to the narrow annual ring width. These findings are expected to serve as fundamental data for future assessment of aged timber and provide useful information for the appropriate reuse of dismantled structural wood.

Strandboards were manufactured and their property was tested under several combinations of strand thickness and resin content in order to determine the effects of strand thickness and resin content on the mechanical and physical properties of the boards. It was shown that strand thickness and resin content had statistically significant effects on bending properties, internal bond strength, and water resistance. Bending properties and water resistance were found to be superior with thinner strands and higher resin content, while internal bond strength was found to be higher for thicker strands with lower resin content and for thinner strands with higher resin content. Statistical analysis confirmed that bending properties were more affected by strand thickness and thickness swelling was more affected by resin content. In terms of bending properties, modulus of rupture improvement due to strand orientation was found to be more pronounced for thicker strands.

In this study, dust explosion of coal or charcoal mixed with wood powder was evaluated using a 1.2 L Hartmann apparatus. For particles smaller than 53 µm, the minimum explosion concentration (MEC) of wood dust and coal was 40 g/m³, indicating a high risk of dust explosion. Charcoal did not explode above 1000 g/m³, it was considered to be caused by the carbon and volatile matter contents. For coal mixed with wood powder, the minimum ignition energy (MIE) of 25 wt% wood was the lowest. Coal was considered to act as a dispersant for wood powder, as the degree of cohesion in 25 wt% wood mixture was almost the same as that in coal. For charcoal mixed with wood, the both MEC and MIE values were decreased depending on the wood powder ratio.

Oaks (Quercus spp.) have a visual characteristic in which broad rays (BR) appear clearly. BR occasionally creates unique band-like patterns, silver grain (Torafu), which cross axial tissues on the quarter-sawn surface and are related to an important visual factor. Therefore, the aim of this study was to extract the patterns formed by BR and classify individual pieces using the BR patterns. First, we prepared the photographic conditions that could document the BR clearly. Then we applied image processing, including percentile binary and anisotropic high-pass filter, to convert the BR regions into binary images. The average extraction accuracy of this method was 96.4% using 20 samples. After measuring some geometric feature parameters on the images and summarizing them using principal component analysis, it was suggested that three feature values—the index of lateral band size against the axial direction, the index of vertical length along the axial direction, and the number of blobs—are important for documenting the BR shapes. Finally, 180 samples were classified into five clusters, “inconspicuous BR”, “diagonal pattern”, “vertical long thin pattern”, “large Torafu pattern”, and “Torafu pattern”, based on the features, confirming that the qualitative classification was carried out effectively.