Cross-scale multimodal modeling of wood mechanical properties: Prediction and mechanistic insights

Le résumé fourni par la source

Accurate evaluation of wood mechanical properties is fundamental to structural design and material grading in construction applications. However, it remains challenging because of wood's intrinsic cross-scale heterogeneity and the limitations of conventional evaluation methods. This study proposes a unified cross-scale multimodal framework that integrates macroscopic physical parameters with microscopic anatomical information to predict key mechanical properties of wood. To evaluate model robustness and generalizability across diverse wood species, a systematic benchmarking scheme comprising a 10 × 10 evaluation matrix of vision backbones and regression algorithms is established. The evaluation results demonstrate that the proposed framework consistently outperforms single-modality baselines, achieving stable predictive performance for compressive strength parallel to grain (CSG), modulus of rupture (MOR), and modulus of elasticity (MOE), with R 2 values exceeding 0.73 and reaching 0.8050. Interpretability analysis further reveals distinct cross-scale governing mechanisms among different mechanical properties. Macroscopic physical attributes provide the primary explanatory basis for CSG and MOE. In contrast, microscopic anatomical features play a substantially greater role in MOR prediction, contributing 38.52% of the total explanatory weight because bending failure is highly sensitive to localized stress concentrations. Furthermore, integrating anatomical features from three orthogonal sections provides a comprehensive representation of the three-dimensional wood structure. This mitigates single-plane biases and ensures more robust predictive performance across diverse species. Overall, the proposed framework provides a practical and interpretable approach to cross-scale prediction and mechanism discovery, with potential applications in high-throughput wood grading and data-driven quality assessment for construction.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.