This work investigated the feasibility of manufacturing composite cross-laminated timber (CCLT) from bamboo and a western hemlock and amabilis fir mix (hem-fir). Bamboo parallel strand lumber (bamboo PSL) was first fabricated as the surface laminae of CCLT. No. 2 and Btr 2×6 western hem-fir dimension lumber was imported from British Columbia (BC), Canada. The lumber was then sorted and further dried to form the inner laminae of CCLT. Multiple 800 × 800-mm CLT billets were fabricated using a commercial single-component polyurethane (PUR) adhesive. Block shear and delamination tests were conducted to examine the bond quality and durability of bamboo-wood CCLT. The results demonstrated that it is technically feasible to manufacture bamboo-wood CCLT with acceptable bond quality (shear strength, wood failure percentage, etc.) and delamination. Further studies should focus on the optimization of process parameters for manufacturing and surface treatment to improve the bond quality and durability and quantify the mechanical performance of bamboo-wood CCLT with a full-size production trial.

This study investigates the ultimate bearing capacity of engineered bamboo columns of varying lengths under eccentric loading using a data-driven machine learning (ML) approach. Although previous studies have investigated engineered bamboo columns under axial or eccentric compression, few have systematically examined the combined effects of load eccentricity and column length. A database containing 432 records was established, with eccentricity, column length, cross-sectional area, compressive strength, and elastic modulus used as input features. Six tree-based ML algorithms were used to develop predictive models, all of which achieved satisfactory predictive accuracy. Shapley additive explanations analysis indicated that the trained random forest (RF) and extreme gradient boosting (XGBoost) models relied most strongly on eccentricity, followed by compressive strength, cross-sectional area, and column length, whereas elastic modulus made the smallest contribution within the range covered by the database. A new functional form was proposed for predicting the ultimate bearing capacity based on the eccentricity ratio, slenderness ratio, stability factor, compressive strength, and cross-sectional area. Two ML-assisted explicit models were developed using predictions generated by the trained RF and XGBoost models. Validation against both the developed database and an independent dataset showed that the explicit model derived from the XGBoost predictions achieved higher predictive accuracy than the RF-based model.