Tailings dams, as essential mining structures, are being built globally for containing the chief waste stream of the mining industry. Catastrophic tailings dam breaches have occurred frequently over the past decade, causing severe impacts on the environment, economy, and human health. The foreknowledge of the tailings dam breach overland flow is crucial for the risk assessment and emergency response planning in order to prevent or minimize possible losses. Using unmanned aerial vehicles (UAV) photogrammetry and smoothed particle hydrodynamics (SPH) numerical method, this study proposed a multidisciplinary procedure for modelling a hypothetical tailings dam breach run out flow over the downstream complex terrain. A case study on a 97-m-height tailings dam in Shandong Province of China was carried out. The proposed procedure was proven applicable to determine the overland tailings flow. The submerged area and flow velocities suggested that the downstream G2 highway would hardly be threatened and more concerns should be paid on the factory plants and workers deployed between the dam toe and the highway. Additionally, the application of UAV photogrammetry in the mining industry as a supplementary surveying method can be further expanded, especially for the numerous small-scale mining sites. The proposed procedure is then recommended for the safety management of the tailings’ storage facilities globally.

Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate that the optimized You Only Look At CoefficienTs (YOLACT) model outperforms in recognizing the beach boundary line, achieving a mean Intersection over Union (mIoU) of 72.63% and a mean Pixel Accuracy (mPA) of 76.2%. This approach shows promise for future integration with autonomously charging UAVs, enabling comprehensive coverage and automated monitoring of BLIs. Additionally, the anti-slide and seepage stability evaluations are impacted by the geometry shape and water condition configuration. The proposed approach provides more conservative seepage calculations, suggesting that simplified 2D modeling may underestimate tailings dam stability, potentially affecting dam designs and regulatory decisions. Multiple numerical methods are suggested for cross-validation. This approach is crucial for balancing safety regulations with economic feasibility, helping to prevent excessive and unsustainable burdens on enterprises and advancing towards the goal of zero harm to people and the environment in tailings management.

Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and mapping. Initially, the ResNet-50 network was employed for the binary classification of the cropped UASP orthophoto images. A comparative analysis was conducted to determine the optimal model between DeepLabv3+ and U-Net. Subsequently, the identified fissures were mosaicked and spatially projected onto the original orthophoto image, incorporating precise projection data, thereby furnishing a spatial reference for environmental governance. The results indicate a classification accuracy of 93% for the ResNet-50 model, with the U-Net model demonstrating a superior identification performance. Fissure orientation and distribution patterns are influenced by the mining direction, ground position of the mining workface, and topographic undulations. Enhancing the CNN performance can be achieved by incorporating variables such as slope indices, vegetation density, and mining workface locations. Lastly, a remote unmanned approach is proposed for the automated mapping of mining-induced fissures, integrated with UAS automated charging station technology. This study contributes to the advancement of intelligent, labor-saving, and unmanned management approaches advocated by the mining industry, with potential for broad applications in mining environmental protection efforts.

Alternative tailings disposal technologies can be effective solutions to mining waste safety and environmental problems. The current decision-making processes for tailings disposal schemes lack consideration of environmental impacts. Based on a case study of an open-pit iron mine in northern China, this study adopted the life cycle assessment (LCA) method to compare the environmental impacts of three tailings disposal schemes of conventional slurry disposal technology (CSDT), dry stack disposal technology (DSDT) by belt conveyance and DSDT by truck transport. The results indicated that (1) the environmental impacts of the CSDT scheme were lowest under the premise that water consumption was ignored; (2) the environmental impacts of the DSDT scheme by belt conveyance mainly originated from its transport process, indicating that the tailings storage facilities (TSFs) site planning could be crucial in design decision making; (3) the environmental impacts of the DSDT scheme by truck transport mainly originated from the energy consumption of dry stacking equipment; and (4) the DSDT scheme by truck transport was eventually found to be preferable and implemented in the case study, after comprehensively considering the LCA results, TSF safety and remaining capacity, and social and policy uncertainties. It is therefore recommended to conduct LCA of environmental impacts in the decision-making process for the sustainable design of TSFs.

Efficient thickening of unclassified tailings slurry (UTS) is critical for enhancing mine backfill efficiency and reducing operational costs. Ultrasonic technology has emerged as a promising approach to facilitating the solid–liquid separation process in such slurries. In this study, systematic experiments were conducted using a 20 kHz ultrasonic concentrator. The effects of ultrasonic treatment timing (applied at 0, 5, 10, 15, 20, 25, 30, and 35 min during free settling) and power (50 to 400 W in eight levels) were investigated by monitoring the solid–liquid interface settling velocity and underflow concentration. The key findings are as follows: Ultrasonic application at the 5 min mark yielded the optimal thickening performance, increasing the final mass concentration by 1.3% compared to free settling alone. The average settling velocity generally increased with ultrasonic power (with the exception of 50 W), and the final underflow concentration exhibited a steady rise. Notably, the 400 W treatment induced a significant settlement acceleration, attributed to the formation of drainage channels. Mechanistic analysis revealed that these drainage channels undergo a dynamic process of formation, expansion, contraction, and closure, driven by ultrasonically induced directional water migration, particle compaction, and energy boundary effects. This research not only enriches the theoretical framework of ultrasonic-assisted thickening but also provides practical insights for optimizing mine backfill operations.