To achieve value-added utilization of domestic bamboo and plantation wood resources, this study investigated the feasibility of using flattened bamboo and Chinese fir for manufacturing cross-laminated bamboo and timber (CLBT). Two types of adhesives, one-component polyurethane (PUR), and phenol resorcinol formaldehyde (PRF), and three applied pressure parameters (0.6, 0.8, and 1.0 MPa) were used to fabricate small CLBT panels (375 mm × 500 mm). In this study, block shear and delamination tests were conducted to examine the bond quality and durability of CLBT panels. The results showed that a significant difference in the bonding shear strength (BSS) in both directions. The bonding shear strength in the minor strength direction (BSS_minor) was 1.81–3.45 times higher than the bonding shear strength in the major strength direction (BSS_major). The adhesive type was the major factor affecting the bond quality and delamination, while the bonding pressure had no significant effect on the bond quality and delamination. Compared with PRF adhesives, CLBT specimens prepared from PUR had higher bonding shear strength (BSS) and wood failure percentage (WFP). However, the durability of delamination specimens prepared by PUR was not as good as layered specimens prepared by PRF.

To identify hygrothermal transfer patterns of exterior walls is a crucial issue in the design, assessment, and construction of buildings. Temperature and relative humidity, as sensor monitoring data, were collected from the outside of the wall to interior bamboo and wood composite sheathing over the year in Huangshan Mountain District, Anhui Province, China. Combining the machine learning method of reservoir computing (RC) with agglomerative hierarchical clustering (AHC), a novel clustering framework was built for better extraction of the characteristics of hygrothermal transfer on the time series data. The experimental results confirmed the hypothesis that the change in the temperature and relative humidity of the outside of the wall (RHT12) dominated the change of the interior sheathing (RHT11). The delay time between two adjacent peaks in temperature was 1 to 2 h, while that in relative humidity was 1 to 4 h from the outside of the wall to interior bamboo and wood composite sheathing. There was no significant difference in temperature peak delay time between April and July. Temperature peak delay time was 50 to 120 min. However, relative humidity peak delay time was 100 to 240 min in April, whereas it was 20 to 120 min in July. The impact formed a relatively linear relationship between outdoor temperature and relative humidity peak delay time. The hygrothermal transfer patterns were characterized effectively by the peak delays. The discovery of the hygrothermal transfer patterns for the bamboo and wood composite walls using the machine learning method will facilitate the development of energy-efficient and durable bamboo and wood composite wall materials and structures.

Laminated bamboo lumber (BLL) and bamboo scrimber (BS) are potential environmentally friendly building materials, considering that they are hygroscopic materials and that their hygrothermal performance is closely related to the occupants’ comfort, their building durability, and building energy consumption. This study carried out material property tests on bamboo-based materials (BLL and BS) and analyzed hygrothermal performance simulations on bamboo exterior wall constructions in five major climatic zones using WUFI. Results show that BS had a greater heat storage capacity than BLL. However, BLL showed a relatively higher moisture sorption capacity, and BLL was more easily permeated by vapor than BS. The water content (WC) of 12 walls of BLL and BS was below 20%. BLL and BS met the requirements of the envelope, and the WC of BS was lower than that of BLL. Based on the results, the walls designed in this study are suitable for the corresponding climate zones. In the cold and severe climate zones especially, the external insulation system walls performed better than the internal insulation system. The external insulation system was recommended for these two climate zones. The design and simulation results of walls made of two types of bamboo-based materials provide a scientific basis for the application of the material.

To improve wood structure processing efficiency, a palletizing robot suitable for loading and unloading glued laminated timber (GLT) has been developed. The robot comprises a six-axis connecting rod mechanism and a sponge sucker as a grasping actuator, which can enable the intelligent automatic loading and unloading and palletizing operations for small-sized GLT. Matlab robotics was used to construct the kinematic model of the GLT loading and unloading robot. Based on Matlab and Monte Carlo methods, the robot workspace was simulated and analyzed to determine the scope of the robot workspace. Using the high-order quintic and sixtic polynomial curve interpolation method, the trajectory of wood structure parts in the process of loading and unloading operations was planned, respectively, under the two conditions of staying and not staying. Tests verified that the simulation results of the pose of the end-effector were consistent with the actual pose of the robot. The robot’s working range could be analyzed intuitively and effectively. The robot’s operation trajectory planning provides data support and a parameter basis for the automatic control and program design of a loading, unloading and palletizing robot.

To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates.