When performing unsaturated seepage flow analysis for unsaturated layers in the ground, a soil water characteristic curve is required. The van Genuchten model is often used to generate this curve, and its parameters are generally identified using gradient methods such as the Gauss–Newton method (G–N method). However, the G–N method is prone to local suboptimal solutions, so in recent years, attempts have been made to apply a random search method using a genetic algorithm (GA method). Here, the van Genuchten model fitting parameters were identified by the G–N and GA methods, based on the results of water retention tests for four soil types, and the identification accuracy of the two methods was compared. As a result, it was confirmed that by setting reasonable initial values, the van Genuchten model constants can be identified with sufficient accuracy using the G–N method, which has a lighter computational load.

Cross-laminated timber (CLT) is attracting attention as a new wood-based material and is expected to contribute to the increased consumption of domestically produced timber, such as sugi (Cryptomeria japonica), and carbon fixation through the use of wood as a building in the city. Sugi CLT are characterized by low shear rigidity and shear strength against bending deformation in orthogonal layers. In general, hardwoods have excellent shearing properties, and when used in orthogonal layers in CLT, the shearing properties are expected to be improved. Therefore, we attempted to improve the shearing performance of CLT using a domestic fast-growing broad-leaved tree, Sendan (Melia azedarach), which has a high carbon fixation capacity. We attempted to improve the shear performance of the CLT by combining sugi and sendan. As a result of measuring the physical properties of Sendan, it was found that it has the same shear strength as beech and better shear performance than softwood such as Sugi. We prototyped a composite CLT made of Sugi and Sendan and conducted bending and horizontal shear tests. Consequently, no shear failure was observed in the composite CLT owing to the bending tests, and an improvement in performance was confirmed. Analysis of the stress distribution in the sandwich panel model showed that the shear stress distribution during bending failure was almost the same for cedar CLT and composite CLT, confirming that the shear failure performance was improved.

The manufacture of softwood plywood does not vary greatly from product to product, and allocation of greenhouse gas (GHG) emissions is often based on the lumber volume of the finished product. However, since products of the same thickness may be characterized by different numbers of veneer layers, an allocation method that considers this may be more appropriate.

Therefore, to evaluate the GHG emissions involved in manufacturing plywood products, this study allocated energy and other inputs to the product-manufacturing process according to the number of layers of veneer. To assess the impact of the allocation method on GHG emissions, the results were compared with those of an allocation method based on the lumber volume of the product. Four products were evaluated: 9-mm-thick × 5 veneer layers, 12-mm-thick × 5 veneer layers, 24-mm-thick × 7 veneer layers, and 28-mm-thick × 9 veneer layers. The system was bound by the raw material procurement and product-manufacturing stages.

The allocation method based on the number of layers showed a 1.44-fold difference in GHG emissions per m3 of product between the product with the highest GHG emissions (9-mm-thick × 5 veneer layers) and that with the smallest thickness (24-mm-thick × 7 veneer layers). This difference was influenced by the number of veneer layers per unit thickness. In comparison, the allocation method based on product volume resulted in a 1.11-fold difference in GHG emissions between the product with the greatest emissions (9-mm-thick × 5 veneer layers) and that with the smallest thickness (24-mm-thick × 7 veneer layers). This difference was influenced by differences in the amounts of adhesive used. Comparing the impact of the different allocation methods, the difference in GHG emissions was greatest for the 24-mm-thick × 7-veneer-layers product, with the result of the allocation method that considered the number of layers being 0.87 times greater than that based on the amount of lumber in the product. This difference indicates that the number of layers per unit has a greater impact on GHG emissions than the lumber volume of the finished product.

In summary, it is clarifying that different allocation methods for the manufacturing process have different impacts on the GHG emissions of the product. Therefore, for allocation in the plywood manufacturing process, the number of layers per unit thickness of veneer should be considered.

Pineapple leaf fiber (PaLF) shows promise as a reinforcement material for polymer matrix composites due to its high tensile strength and Young's modulus, comparable to those of flax fibers. However, due to the non-polar nature of polypropylene (PP), forming a composite with cellulose-rich PaLF is challenging without surface modification. This study investigates the interfacial shear strength between PaLF and PP, focusing on the effects of maleic anhydride modification of PP and fiber treatment with a NaOH solution. Additionally, a method was developed to precisely estimate the circumference of PaLF cross-sections prior to micro-drop tests for measuring interfacial strength. Inaccurate circumference estimation is identified as a potential source of data variability. PaLFs were extracted using a water-jet process, and the circumferences at the micro-drop test site were estimated by measuring fiber width from multiple directions. The correlation between estimated circumferences and measured circumferences showed reasonable accuracy, with errors below 10%. The highest interfacial strength between PaLF and PP was achieved with the addition of 4% maleic anhydride to PP, increasing the interfacial strength by approximately 80% compared to the unmodified case. Immersing PaLF in a NaOH solution significantly improved interfacial strength, even without PP modification.

Carbon Fiber Reinforced Plastics (CFRP) with thermosetting resin matrix are widely used in sports and aerospace industry. From the perspective of carbon neutrality, the development of carbon fiber recycling technology is essential. The hot air circulation recycling method has been reported to deliver recycled carbon fibers (rCF) from CFRP with an epoxy resin matrix in the state of continuous fibers, which have comparable properties to virgin carbon fibers and are bound together with decomposition products derived from the epoxy resin. We have previously reported that the removal of sizing agents from carbon fiber surfaces or oxidation treatment improve the interfacial shear strength (IFSS) between carbon fibers and polyamide resin. Since rCF is obtained by heat-treatment of CFRP under an oxygen environment, it is expected to have superior interfacial bonding properties to thermoplastic resins, which is similar to heat-treated or oxidized carbon fibers. In this study, single-fiber pull-out tests were conducted using carbon fibers and recycled carbon fibers with Polyamide 6 (PA6) to clarify the effect of heat treatment on IFSS. Additionally, three-point bending tests were performed on CF/PA6 laminates reinforced with these fibers to reveal the relationship between mechanical properties of CF/PA6 laminates and IFSS. For rCF, longer heat treatment results in higher IFSS. For three-point bending tests, however, excessively higher IFSS resulted in lower bending strength, suggesting an optimal IFSS value to achieve maximum bending strength. The recycled carbon fibers used in this study showed equivalent flexural strength to laminates reinforced with unsized carbon fibers, indicating that they are suitable for a reinforcing material for thermoplastics composites, including PA6.

N-doped (N- ) DLC films were deposited using plasma CVD method to investigate the effects of substrate bias voltage and nitrogen flow ratio on the film structure, hardness and residual stress, which are important to apply them to machine parts. Nitrogen content in N-DLC films increased as increasing N₂ gas flow ratio, and they decreased as increasing substrate bias voltage. Results of Raman spectroscopy, I (D) / I(G) suggested that sp² clusters tended to increase as nitrogen flow ratio increased. Also, bond angles and bond lengths of the films became typical graphitic structure. And FWHM (G), which is one of the Raman parameters and indicates degree of amorphization of the films, decreased with increasing nitrogen content in the films. However, large N₂ flow rate made the structure of the film to be amorphous. FT-IR analysis suggested that relative proportion of C₂H₂ decreased because of large N₂ flow rate, and number of C-H bonds decreased in the films. Hardness of N-DLC films decreased with increasing nitrogen content of N-DLC films with varying substrate bias voltage. On the other hand, nitrogen content did not concern hardness of the films with varying nitrogen flow ratio. Comparing hardness with FWHM (G), hardness of the films increased with increase of FWHM (G) under various deposition conditions. In addition, hardness decreased with increasing Raman N/S ratio, which indicates hydrogen content in the films. Hardness and compressive residual stress related with not only nitrogen but also hydrogen content in the films.

Fracture analysis is essential for identifying the causes of metal product failures and preventing failure recurrence. This study focuses on automating the segmentation of fracture surfaces, addressing challenges such as overexposure and underexposure clipping in scanning electron microscopy (SEM) images. We propose a deep learning model enhanced with a novel data augmentation technique called clipping augmentation. This technique artificially introduces clipping effects, such as overexposure and underexposure, into the images to improve segmentation accuracy. Our experiments utilized 1000 SEM images of fracture surfaces, labeled at the pixel level by experts. The dataset was divided into training, validation, and test sets. Model architecture evaluations revealed that the Attention U-Net with ResNet50 fine-tuning provided the highest intersection over union (IoU) scores, achieving a remarkable score of 0.933 on test data. Optimal preprocessing included contrast-limited adaptive histogram equalization and pseudo-coloring, significantly enhancing segmentation performance. Clipping augmentation, by tuning the maximum artificial clipping size and the number of artificial clippings per image, markedly improved the model's robustness against actual overexposure and underexposure artifacts. Combined with standard data augmentation techniques, this method significantly improved the IoU, demonstrating the efficacy of our approach in handling complex SEM images. Our results indicate that the proposed method can reliably segment fracture surfaces in SEM images, even under adverse conditions, paving the way for more efficient and accurate fracture analysis automation.