The compressive strength in the major direction of cross-laminated timber CLT is the key to supporting the building load when CLT is used as load-bearing walls in high-rise wood structures. This study mainly aims to present a model for predicting the average compressive strength of CLT and promoting the utilization of CLT made out of planted larch. The densities and compressive strengths of lamina specimens and CLT samples with widths of 89 and 178 mm were evaluated, and their relationship was analyzed to build a prediction model by using Monte Carlo simulation. The results reveal that the average density of the lamina and CLT were about equal, whereas the average compressive strength of the CLT was just about 72% of that of the lamina. Width exerted no significant effect on the average compressive strength of the CLT, but homogenization caused the wider CLT to have a smaller variation than that of the lamina. The average compressive strength of the lamina could be calculated by using the average density of lamina multiply by 103.10, and the average compressive strength of the CLT could be calculated according to the compression strength of lamina in major and minor direction, therefore, a new prediction model is determined to predict the average compression strength of CLT by using the average density of lamina or CLT, the average compression strength of CLT made in this study is about 74.23 times of the average density of the lamina. The results presented in this study can be used to predict the average compressive strength of CLT by using the average density of lamina and provide a fundamental basis for supporting the utilization of CLT as load-bearing walls.

The density and modulus of elasticity (MOE) distribution can provide information on the effectiveness of parametric and non-parametric methods in calculating the characteristic value of MOE. In this study, we aim to determine the optimal distribution model of the actual measured data of the lumber. We also estimate the lumber’s MOE characteristic value and compare the difference in density and MOE between natural and planted larch. Approximately 1200 pieces of dimension lumber of 4 m × 140 mm × 40 mm in size, made from larch and planted larch, were obtained, tested, and the corresponding standard MOE value was calculated. Results revealed the 3-parameter Weibull distribution to be optimal in fitting the natural and planted larch distributions. The parametric method proved effective in calculating the characteristic value of both larch groups, with characteristic MOE values of 9.73 kN/mm² and 8.84 kN/mm², and characteristic density values of 530 kg/m³ and 460 kg/m³ for natural and planted larch, respectively. Moreover, the MOE and density values followed grades C40 and C35. Thus, the conclusion is that the parametric method should be used to determine these characteristic values for natural and planted larch.

Machine learning methods have demonstrated significant advantages in predicting the shear mechanical performance of concrete connectors. However, the application to nailed connections in wood structures remains limited. Accurate prediction of the shear capacity of nailed connections is essential for assessing the seismic performance and safety of wood structures. In this study, a series of push-out tests were conducted on panel-sheathing nailed connections, and the obtained load–displacement curves were analyzed to characterize their mechanical behavior. Six ML models were trained and tested using a dataset comprising 101 push-out tests. Eight key features, including both mechanical properties (E, G, f_es, f_em) and geometric parameters (d, l, s, D), were selected as input parameters, and three characteristic values including ultimate load, initial shear stiffness, and ultimate displacement were chosen as output parameters. The results indicated that the support vector regression (SVR) model exhibited the best performance in predicting the three output parameters of nailed connections, with corresponding R² values reaching of 0.9950, 0.9976, and 0.9994, respectively. The study employed the Shapley additive explanations (SHAP) method to investigate the importance of features. The findings revealed that the elastic modulus of the side plate significantly influenced ultimate load and initial shear stiffness. Additionally, the initial shear stiffness was primarily affected by the nail spacing, whereas the shear modulus and pin-bearing strength substantially affected ultimate displacement. The prediction results of the machine learning model were compared with existing empirical, confirming that the machine learning model achieved high accuracy and strong applicability in predicting the shear bearing capacity of nailed connections in wood structures.