Junpeng Huang

Manual joint surveys on open-pit mine slopes are high-risk and inefficient, whereas conventional remote sensing and laser scanning are costly and limited in fine-crack detection. To enable low-cost, high-precision, noncontact structural plane interpretation, this study targets the southern slope of the Shitoumei No. 1 open-pit coal mine and presents a joint recognition method that integrates unmanned aerial vehicle (UAV) close-range photogrammetry with an octree-based adaptive statistical outlier removal (SOR) filter using two-layer locality-sensitive hashing (2 L-LSH). High-overlap close-range images from real-time kinematic (RTK)-equipped UAVs are reconstructed into centimeter-scale dense point clouds. An octree-based multiscale density-driven adaptive filter, combined with 2 L-LSH for accelerated nearest-neighbor search, resolves the noise‒crack preservation trade-off caused by uneven point–cloud density and enhances computational efficiency. Four clustering algorithms, namely, discontinuity set extractor (DSE), K-means, region growing, and fuzzy C-means (FCM), were compared; FCM was optimized to semiautomatically extract five dominant joint sets with orientations, trace lengths, and spacings. Compared with the conventional SOR filter, the proposed method achieves a 188-fold increase in computational speed and yields a 126% improvement in comprehensive filtering performance. The optimized FCM achieves a mean orientation deviation of 3.2° and a misclassification rate of 3.3%. This workflow enables safe and efficient acquisition of rock-mass joint geometric parameters, as demonstrated for the investigated Shitoumei No. 1 open-pit mine site.

Determining the safe thickness of a boundary crown pillar is critical during the transition from open-pit to underground mining, as it directly affects both mining safety and resource recovery. Crown pillar instability is commonly associated with asymmetric stress redistribution, nonuniform deformation, and progressive plastic failure. In this study, the Kumusayi Li-Nb-Ta mine in Xinjiang, China, was selected as an engineering case to optimize the boundary crown pillar thickness and evaluate its deformation characteristics. Four theoretical methods, namely the load transfer intersection method, span-to-thickness ratio method, simplified structural beam method, and Rubeneeite formula method, were first used to determine the feasible thickness range. The calculated thicknesses were 19.99, 14.00, 29.81, and 10.41 m, respectively, yielding an engineering design interval of 14.00–29.81 m. Based on this interval, four thickness schemes of 15, 20, 25, and 30 m were evaluated using FLAC3D simulations in terms of stress redistribution, displacement evolution, surface movement, plastic-zone development, and deformation symmetry. The results show that the 15 m pillar exhibits pronounced stress concentration, asymmetric deformation, and through-going plastic failure, indicating insufficient stability. Although the 20 m pillar improves the load-bearing capacity, a potential connected failure path remains. At 25 m, the high-stress zone becomes localized, the plastic zone no longer penetrates the pillar, and the maximum vertical displacement decreases by approximately 27.0% compared with the 15 m scheme. Increasing the thickness to 30 m provides limited additional improvement, with less than a 2% reduction in maximum vertical displacement compared with the 25 m scheme. Physical similarity model tests further confirm that a 20.8 cm model pillar, corresponding to a 25 m prototype pillar, effectively prevents through-going cracking and overall slope sliding. Therefore, a 25 m boundary crown pillar is recommended for the Kumusayi mine.

The spatial variability of geotechnical parameters, such as soil shear wave velocity (V_s), exhibits significant nonlinearity and non-stationarity with respect to depth (h) due to the influence of overlying stress. Existing stochastic field models for describing the variability of geotechnical parameters are insufficient for simultaneously capturing both the nonlinearity and non-stationarity of these parameters. In this study, a power function V_s = V_s0[f(h)]ⁿ is proposed to describe the nonlinear trend in soil shear wave velocity (V_s) as a function of depth-related variable f(h). Considering the physical significance and correlation of the power function parameters V_s0 and n, the variability of these parameters is modeled using a random variable model and a stationary stochastic field model, respectively. This leads to the development of a non-stationary stochastic field model that describes the spatial variability of V_s. The proposed method is applied to simulate the random V_s-structure of a seabed site in China, and the obtained V_s results are used to assess the liquefaction probability of the seabed. The results indicate that ignoring the correlation between geotechnical parameters significantly increases the variability of the final simulation results. However, the proposed method accurately captures the nonlinear trend and non-stationary characteristics of soil V_s with depth, and the liquefaction probability predictions are consistent with those derived from in situ V_s measurements in the study area. This approach provides valuable guidance for simulating the spatial variability of depth-dependent geotechnical parameters, particularly those significantly influenced by overlying pressure.

Exposed to seasonal climate changes, the loess in the Ili region of Xinjiang, which has variable engineering properties, frequently undergoes freezing–thawing (F-T) and wetting–drying (W-D) cycles. In the present research, a series of uniaxial compression tests were conducted to investigate the collapsibility characteristics of the representative loess slope in the Ili region. In parallel, scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR) tests were conducted. The test results obtained from the research indicated that both F-T cycles and W-D cycles exacerbate the deterioration of the loess, with the most severe effects observed after 6–10 cycles. Under the combined physical cycles, the microstructure of the loess progressively evolves from the relatively aggregated state to the dispersed one. Meanwhile, the porosity of the loess exhibited an initial increase with the number of W-D cycles, followed by an obvious decrease. Note that the pattern of the loess experiences fluctuation, which was achieved at the given point with the increased number of F-T cycles. It is suggested that the variability in loess wetting collapse is attributed to the irreversible alteration in the microstructure attributed to the combined cycles. The main reasons for the occurrence of loess collapse are the frost heaving force and the swelling–shrinking action. The impacts of W-D and F-T cycles on the loess obtained from this research can make a contribution to the in-depth understanding about loess collapse in the Ili valley.