Cross-Laminated Timber made of Unmodified and Thermally Modified Yellow-Poplar Lumber

Abstract

Cross-laminated timber (CLT), a sustainable construction material, is transforming the construction industry. To meet diverse demands, especially for exterior applications, its durability must be improved to withstand conditions ranging from dry indoor environments to moisture-prone settings. Thermally modified wood (TMW) is a potential solution to enhance CLT's moisture durability and reduce moisture-related issues. Yellow-poplar (YP), abundant in the Appalachian region, is a promising raw material due to its availability and favorable mechanical properties and incorporating its TMW into CLT outer layers offers a sustainable strategy to improve moisture resistance, reduce moisture-induced strain, and enhance durability for exterior applications. The goal of this work was to enhance the moisture durability of CLT by integrating TMW in its outer layers. This goal was addressed through four objectives: (1) determining the water vapor permeability and resistance factor of CLT made from unmodified wood and hybrid CLT with TMW outer layers, including the influence of the one-component polyurethane (PUR) adhesive layer; (2) predicting long-term moisture diffusion performance using hygrothermal simulations; (3) establishing correlations between moisture-induced strain and water vapor diffusion to provide input for physics-informed modeling; and (4) developing a predictive model for water vapor permeability and resistance factor (µ-value) that accounts for swelling strain and the adhesive layer. The first objective was achieved by investigating steady-state moisture diffusion (ASTM-E96) through CLT panels of various configurations, including 3-layer, 2-layer (to evaluate bondline effects), and 1-layer boards for both unmodified and hybrid CLT. Results showed that hybrid CLT exhibited significantly improved moisture resistance compared to unmodified CLT, with µ-values of 51.3 versus 32.7 for unmodified 3-layer CLT, and contributions from TMW (µ = 32.9) and PUR adhesive (µ = 1345), compared to 22.3 for unmodified wood. The second objective involved simulating long-term moisture diffusion using WUFI software. While simulations indicated no significant improvement in moisture resistance for hybrid CLT compared to control, likely due to WUFI's lower sensitivity at low µ-values, this confirmed the absence of moisture accumulation in the middle layer of hybrid CLT, consistent with experimental results. Simulations also evaluated various insulation options, identifying Polyisocyanurate board (R=72) as an effective single-layer interior insulation. The third objective focused on the correlation between moisture diffusion and moisture-induced strain. Dimensional changes were monitored in 1-layer boards, 3-layer and 5-layer CLT samples under controlled conditions, revealing strong relationships, such as R² = 0.99 between diffusion and volumetric strain in unmodified wood. Swelling strain, which reduces pore sizes in the microstructure, reduced diffusion by an average of 77%. Outdoor weathering over one year showed no delamination in the 5-layer hybrid CLT, while control YP CLT exhibited significant delamination on the surface and thickness; minor surface checks on TMW were attributed to lower elasticity. These results confirm that hybrid CLT with TMW outer layers offers superior long-term durability for outdoor applications. Finally, the fourth objective was accomplished by developing a physics-informed data-driven machine learning (PIDDML) model to capture the interplay between diffusion, adhesive layers, and moisture-induced strain. Traditional Fick's law models fail to account for these effects, resulting in large prediction errors. The PIDDML model, trained on 11 features including humidity, temperature, and moisture, integrated diffusion and strain data, explicitly accounting for swelling and adhesive effects. It outperformed Fick's law, achieving a maximum absolute error (MAE) of 1.4–13.1% for spruce-pine-fir (SPF) CLT and 11.2% for hybrid CLT compared to 46.7–66.0% under Fick's law, with an overall R² of 0.96. Using a logistic regression moisture content-permeability submodel, the framework was extended to other wood species, generating data for untested species and achieving cross-validation R² = 0.94. By embedding physical constraints such as Fick's law, mass conservation, and absorption–diffusion kinetics, the PIDDML model ensures physically plausible predictions, reduces reliance on extensive experimental data, and generalizes across diverse conditions and species. By integrating experimental measurements, hygrothermal simulations, and advanced PIDDML modeling, this study provides a robust framework for predicting CLT's moisture behavior. The hybrid CLT approach enhances dimensional stability and durability, advancing its use in moisture-exposed construction applications, contributing to sustainable building practices, and supporting wider adoption of CLT in North America.