Automatic Segmentation of Pipe Flow Core and Multi-Parameter Optimization of Cemented Tailings Backfill: A Solid-Waste-Utilization Approach via a Cascaded K-GVF Snake Model

In green mine construction and bulk solid-waste utilization, pipeline transport energy efficiency and stope support stability of unclassified cemented tailings backfill exhibit a critical cross-scale mechanical trade-off. However, key process parameters are traditionally determined by empirical heuristics, lacking automated multi-objective quantitative evaluation. To address scale-bar interference and complex boundary degradation in industrial CFD velocity contour maps, this study proposes a physically constitutive-constrained HSV-K-GVF-Snake graded active contour model. Utilizing Bingham three-phase flow priors, the algorithm automatically extracts the cross-sectional flow-core area ratio as a macroscopic geometric proxy for fluid shear-dissipation trends, while three-dimensional micro-porosity is quantitatively resolved via industrial Computed Tomography (CT) reconstruction. Tested across 100 complex contour maps, the proposed segmentation algorithm achieves an Intersection over Union (IoU) of 85.61%, a Dice coefficient of 92.2%, a root mean square error (RMSE) of 0.118, and an average processing time of 1.2908 s per image, reducing GVF iteration steps by over 60% compared with unguided models. Multi-factor analysis across 120 simulated configurations indicated that slurry concentration was the dominant factor affecting the flow-core ratio and the CT-derived porosity indicator. Under the adopted CQI formulation and investigated parameter combinations, the condition with a pipe diameter of 180 mm, a slurry mass concentration of 76%, and a piping aspect ratio of 5 achieved the highest compromise score between the proxy-based transport-efficiency indicator and the porosity-based compactness indicator.