A generalized two-step model for predicting thermal conductivity of bentonite-based mixtures in nuclear waste repositories

Thermal conductivity (TC) of buffer/backfill materials critically governs high-level radioactive waste repository performance. Based on the geometric mean method and effective medium theory (EMT), a “two-step” TC prediction model for bentonite-based mixtures was proposed. A solid-phase correction factor ( c) and water–air balance factor ( z) are introduced to adjust the contribution weights of the solid, liquid, and gas phases to the TC of the matrix (bentonite–water–air). The EMT was then employed to embed additives as a dispersed phase into the matrix, establishing a computational framework for mixture TC. Based on 538 sets of measured TC for bentonite-based mixtures, the predictive performance of the new model and existing prediction models was evaluated. The results indicate that for both single and multi-component bentonite-based mixtures, the new model demonstrates significantly superior predictive accuracy compared to existing models. The R 2 values of existing models remain below 0.6 (e.g., differential effective medium theory model 0.536 for bentonite–graphite mixtures, improved geometric mean model 0.576 for bentonite–graphene oxide mixtures, and up to 0.581 on the entire database), whereas the proposed model achieves R 2 values of 0.836–0.979 across specific mixtures and 0.868 for the entire database, demonstrating a substantial improvement in predictive accuracy. By incorporating parameters c and z, the new model accurately captures the combined influence of individual phases on mixture TC. The model also exhibits enhanced universality for predicting the TC of diverse mixtures and effectively elucidates the influence patterns of key factors on thermal performance.