Guangyin Lu

Ground subsidence and surface collapse induced by underground mining have long been central concerns in rock mechanics and mining engineering. Such failures disrupt stratigraphic integrity and pose severe threats to local ecosystems and the lives and property of mine personnel. This study, focusing on the Pulang Copper Mine in Yunnan Province, employs a three-dimensional numerical modelling approach to investigate surface deformation resulting from block-caving operations. A high-resolution 3D geological model was constructed to elucidate the mechanisms driving ground movement, particularly emphasising the collapse evolution above the southern mining district following extraction in the initial caving panel. Simulation results indicate that continued mining in the district’s south progressively enlarges the overlying subsidence basin. Comparison of simulated displacements and collapse extents with field measurements confirms the reliability of the calibrated model. Based on this, we forecast potential surface collapse zones associated with future mining schedules, providing quantitative guidance for mine safety management and environmental protection.

Groundwater drainage-induced karst collapse is a major geohazard in coal-mining regions of central Hunan, threatening residential safety and infrastructure. This study focuses on the Tuoshan minefield in the Jinzhushan mining area by integrating multi-source field data, including surveys of 170 collapse points, long-term groundwater monitoring at six boreholes, and high-density electrical geophysics. A topographically corrected MODFLOW seepage-field model is developed and calibrated for 2014 (RMSE = 0.32 m; NSE = 0.85) and validated for 2015–2016 (RMSE = 0.41 m; NSE = 0.81). To address the large groundwater-level simulation errors commonly encountered in subtropical hilly karst mining settings, the model incorporates a topographic correction, improving simulation accuracy by 12% relative to an uncorrected model. The simulations capture rapid “steep rise–slow fall” groundwater dynamics: Heavy rainfall (>100 mm/day) raises groundwater levels by 2.8–3.1 m within 2–3 days, whereas pumping (200 m³/h) causes a 1.9–2.2 m decline within one week. A 1.2 km drawdown funnel forms and overlaps with 89% of collapse points, indicating that seepage-field evolution and groundwater-level decline control collapse clustering, with soil suffusion and soil–water–rock interaction acting as key amplifying processes. Based on Terzaghi’s effective stress principle and the Theis solution, a collapse prediction formula is derived and validated using measured events (accuracy = 87.5%), and a region-specific critical hydraulic gradient (i_n = 0.85) is determined, lower than values reported for North China. The proposed workflow provides quantitative thresholds and model-based guidance for karst collapse prevention in subtropical mining areas.

Covered karst collapse is a key geotechnical hazard in infrastructure construction in karst regions of China. In particular, strata consisting of an overlying clay layer and an underlying sand layer are prone to abrupt collapse induced by sand leakage under construction disturbances, which poses serious risks to pile foundation safety. To clarify the disaster-forming mechanism and develop a quantitative analysis method, this study investigates the mechanical behaviour of the entire collapse process by combining theoretical analysis with numerical simulation. A continuous mechanical analysis framework is established that follows the sequence from sand layer leakage to cavity expansion and then clay layer instability. Within this framework, a calculation model for the angle of repose of the sand layer is proposed that considers seepage and confined pressure effects. Simultaneously accounting for the influence of the casing, stability models for overall and localised collapses are developed using limit equilibrium theory. A comprehensive safety factor criterion K_c based on the critical span (or radius) is then proposed, leading to a linked evaluation method that couples the potential span of the sand layer with the ultimate span of the clay layer. The results show that an increase in Δh/h significantly reduces the angle of repose of the sand layer; the mechanical mechanism is confirmed whereby an increase in the roof span leads to shear stress exceeding the soil’s shear strength, thus triggering instability; the proposed safety factor K_c can effectively predict both overall and localised collapse, and case verification demonstrates that the predicted spans match well with actual collapse dimensions. The results provide a theoretical and technical basis for risk prediction, as well as for the prevention and control of pile foundation construction in karst areas.

The spatial characteristics of the dry beach in tailings ponds are critical indicators for the safety assessment of tailings dams. This study presents a method for dry beach extraction that combines deep learning-based semantic segmentation with 3D reconstruction, overcoming the limitations of 2D methods in spatial analysis. The workflow includes four steps: (1) High-resolution 3D point clouds are reconstructed from UAV images, and the projection matrix of each image is derived to link 2D pixels with 3D points. (2) AlexNet and GoogLeNet are employed to extract image features and automatically select images containing the dry beach boundary. (3) A DeepLabv3+ network is trained on manually labeled samples to perform semantic segmentation of the dry beach, with a lightweight incremental training strategy for enhanced adaptability. (4) Boundary pixels are detected and back-projected into 3D space to generate consistent point cloud boundaries. The method was validated on two-phase UAV datasets from a tailings pond in Yunnan Province, China. In phase I, the model achieved high segmentation performance, with a mean Accuracy and IoU of approximately 0.95 and a BF of 0.8267. When applied to phase II without retraining, the model maintained stable performance on dam boundaries, while slight performance degradation was observed on hillside and water boundaries. The 3D back-projection converted 2D boundary pixels into 3D coordinates, enabling the extraction of dry beach point clouds and supporting reliable dry beach length monitoring and deposition morphology analysis.

Bearing vibration data are often contaminated with noise, which is detrimental to equipment fault diagnosis and predictive maintenance. Denoising bearing vibration data is crucial. Traditional denoising methods have certain limitations. For instance, when employing wavelet denoising, fixed basis functions may fail to perfectly match all signal structures, potentially compromising denoising accuracy. Similarly, when utilizing data-driven tight frame (DDTF) denoising, the learned basis, due to the lack of noise constraints, may incur a risk of overfitting. To optimize denoising performance in both scenarios, this paper proposes a method that combines wavelet transform and DDTF dictionary learning to extract noise based on a doubly sparse dictionary. The specific approach involves mutually cascading the wavelet transform and DDTF. After applying the wavelet transform to the noisy signal, multi-layer wavelet sparse coefficients are obtained. DDTF processing is then applied to each layer of wavelet sparse coefficients. Subsequent inverse transformation achieves noise suppression. This method integrates the structural constraint capability of wavelet decomposition with the learning capability of DDTF, thereby mitigating their respective limitations to some extent. The denoised data are fed into a residual network model, and training results confirm that the proposed method achieves the best classification performance. Experimental results from both data denoising and deep learning classification demonstrate that the proposed method exhibits superior denoising performance. Although the algorithm structure of this method is more complex compared to other approaches, it is meaningful in scenarios where high-precision denoising is required.

The construction of bridge pile foundations in karst regions faces significant risks, including karst collapse and ground subsidence caused by dewatering during excavation. Both karst collapse and shallow soil cavity subsidence are influenced by numerous factors, which significantly complicates the risk assessment of bridge pile foundation construction in these areas. This study proposes a risk assessment method for bridge pile foundation construction in karst regions based on the Fuzzy Analytic Hierarchy Process (FAHP). An evaluation index system for pile foundation construction risks was established through experimental research, expert surveys, and data analysis. Additionally, the concept of fuzzy mathematics was introduced to quantify the weights of the indicators, enabling the comprehensive assessment of multi-source risks. Experimental results from bridges across the project area demonstrate that this method exhibits a certain level of reliability in assessing the risks of bridge pile foundation construction in karst regions, with the evaluation results aligning well with actual conditions.

Dry beach parameters are important indicators for measuring the safe operation of tailings ponds. The Unmanned Aerial Vehicle (UAV) is widely used in construction, disaster monitoring, and agriculture. In this paper, a new semi-automatic method is proposed that measures the length and slope of the dry beach in a tailings pond using the point cloud obtained through the 3D reconstruction of UAVs. The programs of dry beach boundary identification, point cloud profile denoising, dry beach length, and slope measurement were established. The proposed method was applied to a case study to monitor the length and slope of a tailings pond dry beach. The results show that the maximum error of the dry beach length measurement is 2.25%, and the error of the slope measurement is 2.67%. The error is within a reasonable range, which can be accepted in practical applications, demonstrating the reliability and accuracy of the proposed method. Compared with traditional methods and image-based dry-beach-monitoring methods, the point cloud model provides comprehensive coverage of the entire dry beach. It enables the measurement of the length and slope of the dry beach at any point while offering an intuitive representation of its surface morphology and topographic features.