Yunlong Shu

Waste dump slopes may appear stable after an initial landslide, yet their renewed instability potential under subsequent hydraulic disturbance can remain significant. This study investigates a landslide-affected waste dump slope in Ma’anshan City, Anhui Province, China, by integrating pre-landslide and post-landslide UAV-based three-dimensional reconstruction, CAD-derived representative profiles, field investigation, and measured pre-failure rainfall data. Two-dimensional coupled seepage-stability models were established in Midas GTS NX, and the shear strength reduction method was used to evaluate slope responses under pre-landslide natural and rainfall-infiltration conditions, post-landslide natural and surface-reaching phreatic-line conditions, and reinforced conditions. The results show that, under the adopted upper-bound effective infiltration ratio, the factor of safety of the pre-landslide slope decreased from 1.17 under natural conditions to 0.98 after the recorded rainfall process, indicating that the slope could evolve from a low-reserve stable state to a near-limit or unstable state under high effective infiltration. After the initial landslide, the reconfigured slope reached temporary equilibrium with a factor of safety of 1.55 under natural conditions, but decreased to 0.92 under the prescribed surface-reaching phreatic-line scenario, revealing its conditional renewed-instability potential under an upper-bound hydraulic state. The numerically interpreted instability-controlled section showed good profile-scale agreement with the UAV-derived landslide morphology, with a rear-scarp offset of 0.3 m and a main affected-zone length error of 7.7%. For the reconfigured residual slope, a stabilization scheme dominated by prestressed anchor cables and assisted by local shallow bolts increased the factor of safety to 2.28 under natural conditions and 1.28 under the surface-reaching phreatic-line scenario. The proposed framework provides a practical basis for post-landslide reassessment and residual slope stabilization of rainfall-sensitive waste dump slopes.

Understanding the short-term underwater settling behavior of classified tailings is important for interpreting deposit formation in ponded areas. One-dimensional settling-column tests were conducted for 36 h to examine how fine-particle content (<0.075 mm) and nominal slurry mass concentration were associated with soil-water interface evolution, visually defined settling phases, and cumulative interface displacement. The observations showed an early rapid-settling period, a post-interface settling/compression period, and a later low-rate compression period. After 36 h, the columns displayed a clear-water zone above a visually layered deposit. Across the six treatment means, 36 h cumulative settlement decreased as fine-particle content or nominal mass concentration increased. These are time-specific descriptive trends, not evidence of lower ultimate settlement: equilibrium was not demonstrated, only duplicate runs were performed, and the archived concentration-series records do not contain the run-specific initial volume, height, or component masses needed to isolate a concentration effect. No rheological, pore-pressure, microstructural, strength, or water-content measurements were made. The results therefore describe short-term behavior in a static laboratory column and should not be directly extrapolated to field-scale tailings facilities.

Structural health monitoring (SHM) of underground hydraulic structures such as drainage culverts in tailings ponds is critical for ensuring tailings-dam safety, yet conventional manual inspections and surface-based techniques struggle to capture internal deformations in dark, confined, and partially occluded environments. This study proposes a light detection and ranging (LiDAR)-based deformation-detection framework for SHM of drainage culverts that combines multi-temporal point clouds, cross-sectional geometric fitting, and an improved density-based spatial clustering of applications with noise (DBSCAN)-based clustering strategy. An initial LiDAR scan is used to construct a baseline three-dimensional model of the culvert, and subsequent LiDAR-simultaneous localization and mapping surveys are registered to this reference. Uniform cross-sections are extracted along the culvert axis, and Random Sample Consensus-based fitting provides reference functions to quantify radial deformation at the point level. Candidate deformation points exceeding a noise-informed threshold are then aggregated into physically meaningful deformation sections using a culvert-oriented adaptive DBSCAN strategy, which combines k -nearest-neighbor-based local neighborhood scaling, local-density-based core point screening, and engineering constraints on cluster size and axial span to improve robustness under uneven point density and partial occlusion. The framework is validated in a laboratory corridor and in the drainage culvert of the Luohe tailings pond. The method achieves mean absolute errors of approximately 6.68 and 9.67 mm, respectively, and reduces false deformation sections while correctly localizing all simulated deformation zones. These results demonstrate that the proposed approach provides robust localization and engineering-screening capability, with centimeter-level quantitative indication of deformation, for automated non-contact SHM of drainage culverts.