Biological durability of Guyanese fibre insulation boards in tropical context - part 2: Predicting fungal and termite resistance using explainable machine learning

Abstract

The French Guianese Forest, covering over eight million hectares, represents a key resource for sustainable construction in French Guiana, where demand is rising. Residual wood offers opportunities for bio-based insulation panels; however, applications remain limited by lack of knowledge about biological durability in hot, humid, and biodiverse tropical environments. Few studies quantify how chemical composition and physical properties affect resistance to termites and fungi, and predictive approaches are scarce. This study applied supervised machine learning regression to predict panel mass loss from microorganism attacks, using SHapley Additive exPlanation (SHAP) analysis to interpret interactions between chemical composition, panel density, and biological degradation. Ten types of fibre-insulation panels, produced from sawmill residues, fast-growing species, and plantation wood, were tested under controlled tropical conditions. SHAP analysis identified the holocellulose-to-lignin ratio as the primary driver of degradation, with higher ratios increasing susceptibility to biological attacks. Extractives and moisture content significantly influenced Pycnoporus sanguineus resistance, whereas density had minimal effect on Reticulitermes flavipes and Cryptotermes dudleyi damage. Panels with high extractive content and low holocellulose-to-lignin ratios showed the best durability. These findings provide a data-driven framework for optimising tropical bio-based insulation materials by prioritising chemical composition, supporting sustainable construction and circular economy in French Guiana