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 dam failure accidents occur frequently, causing substantial damage and loss of human and animal life. The prediction of run-out tailings slurry routing following dam failures is of great significance for disaster prevention and mitigation. Using satellite remote sensing digital surface model (DSM) data, tailings pond parameters and the advanced meshless smoothed particle hydrodynamics (SPH) method, a 3D real-scale numerical modelling method was adopted to study the run-out tailings slurry routing across real downstream terrains that have and have not been affected by dam failures. Three case studies, including a physical modelling experiment, the 2015 Brazil Fundão tailings dam failure accident and an operating high-risk tailings pond in China, were carried out. The physical modelling experiment and the known consequences were successfully modeled and validated using the SPH method. This and the other experiments showed that the run-out tailings slurry would be tremendously destructive in the early stages of dam failure, and emergency response time would be extremely short if the dam collapses at its full designed capacity. The results could provide evidence for disaster prevention and mitigation engineering, emergency management plan optimization, and the development of more responsible site plans and sustainable site designs. However, improvements such as rheological model selection, terrain data quality, computing efficiency and land surface roughness need to be made for future studies. SPH numerical modelling is a powerful and advanced technique that is recommended for hazard assessment and the sustainable design of tailings dam 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.

Cemented coal gangue paste backfill (CCGPB) containing coal gangue and fly ash is a backfilling technique newly developed in coal mines in China that allows environmentally hazardous products, such as gangue and fly ash, to be reused in underground stopes. CCGPB materials provide efficient ground support for the caving of strata and reduce surface subsidence. In this paper, field monitoring of CCGPB properties was conducted in an underground coal mine, which mainly included the measurement of the longwall face temperature, humidity, CCGPB internal hydration temperature, stress conditions inside the backfills, and displacement. First, the components of the backfills, paste technique, slurry generation procedures, coalfield geology, and mining conditions were introduced. Then, a monitoring system was designed in the field. An online monitoring system was installed. The results of the field monitoring showed that the curing temperature significantly varied, i.e., from 26°C near the main gate to 37°C near the tailgate. The curing humidity had the same trends, increasing from 60% relative humidity (RH) near the main gate to 81% RH near the tailgate. The internal hydration process of the paste was divided into four stages, i.e., the rapid hydration stage, slower hydration stage, rapid decline hydration stage, and relatively stable stage. The highest hydration temperature was 50°C, which was measured on the second day after the backfill process. The temperature approached stability at 41°C. The evolution of the roof stress applied on the CCGPB was divided into four stages: the development stage, regulation stage, rapid growth stage, and relatively stable stage. The maximum roof loading was 12 MPa in the middle of the longwall face. The deformation of the backfill experienced four stages, i.e., the rapid deformation stage, slow deformation stage, relatively stable stage, and long‐term stable stage. The maximum deformation was 104.3 mm, appearing in the middle of the face. In addition, the compression ratio of the backfill was approximately 4%. The results of this study showed that the working conditions of backfills in the field were different from those in the laboratory. This paper provides guidance for the design of the CCGPB technique and the predictions of surface subsidence induced by the production process of underground mining.