Generalized probabilistic models for performance assessment of bio-based insulation materials in sustainable constructions

The widespread adoption of sustainable, bio-based insulation is hindered by the inherent variability of its properties, making reliable performance prediction a significant challenge for engineers. To address this problem, this study develops a robust probabilistic framework to predict performance while explicitly accounting for this uncertainty. The methodology is based on a comprehensive literature review of 266 studies to establish a performance database for thermal conductivity ( λ ), Noise Reduction Coefficient ( N R C ), and carbon footprint ( C F ). Bayesian statistical methods were then applied to derive generalized predictive models from this data. The findings reveal that λ correlates non-linearly with mass density ( ρ ), while N R C is a nonlinear function of both ρ and thickness ( t ). The carbon footprint is characterized using material-specific uniform and normal distributions, confirming the net carbon sequestration potential of key bio-based options. The main conclusion is that performance can be reliably predicted when variability is statistically quantified. The key contribution of this work is a practical framework that enables designers to make informed material selections, accounting for performance variability and environmental impact. The novelty lies in the creation of generalized, data-driven models applicable across a wide range of bio-based materials, providing a valuable tool to streamline the design of sustainable buildings and support the transition to a low-carbon built environment. • Generalized models predict bio-based insulation performance with uncertainty. • Bayesian analysis links density to thermal and acoustic properties. • Probabilistic models quantify the carbon footprint of bio-based materials. • Supports informed material selection for sustainable building design.