A hierarchical adaptive strategy for selecting rheological models in intelligent backfill mining pipeline transportation systems

Abstract

Reliable prediction of rheological behavior is essential for intelligent and precise backfill transportation systems. However, the widely adopted linear assumption in current hydraulic calculation and control algorithms is increasingly challenged by the coupled effects of high geothermal gradients and high solid concentrations in deep mines. In particular, a quantitative criterion is still lacking for identifying when the linear model becomes inadequate, and a nonlinear constitutive description is required. This study investigates the rheological behavior of high‐concentration cemented slurry under coupled conditions of temperature (20–80°C), fly ash (FA) content (10%–50%), and water‐cement ratio (0.75–1.50). A full‐factorial experimental design was employed, and the model fidelity gain (Δ R ² ) was introduced to quantify the fitting gain obtained when replacing the linear Bingham model with the nonlinear Herschel–Bulkley model. The results show that model applicability is strongly condition‐dependent, and more pronounced nonlinear responses were observed around 40°C and 40% FA under the current experimental design. Based on the present material system, rheological test conditions, and engineering considerations, two recommended Δ R ² thresholds of 0.05 and 0.10 were proposed to distinguish the linear safe zone, the transition zone, and the nonlinear mandatory zone. On this basis, a three‐level model‐selection framework was established to balance computational efficiency and physical fidelity. The proposed method provides a practical basis for rheological model selection in intelligent backfill pipeline transportation systems.