There has been a frequent occurrence of tailing dam failures in recent years, leading to severe repercussions. Flood overtopping is an important element contributing to these failures. Nevertheless, there is a scarcity of studies about the evolutionary mechanisms of dam breaches resulting from flood overtopping. In order to fill this knowledge vacuum, this study focused on the evolutionary characteristics and triggering mechanisms of overtopping failures, utilizing the Heshangyu tailings pond as a prototype. The process of overtopping breach evolution was revealed by the conduction of small-scale model testing. A scaled-down replica of the tailings pond was constructed at a ratio of 1:150, and a controlled experiment was conducted to simulate a breach in the dam caused by water overflowing. Based on the results, the following conclusions were drawn: (1) The rise in water level in the pond caused the tailings to become saturated, leading to liquefaction flow and local slope sliding at the initial dam. If the sediment-carrying capacity of the overflowing water exceeded the shear strength of the tailings, water erosion would accelerate landslides on the slope, generating a sand-laden water flow. (2) The breach was primarily influenced by water erosion, which subsequently resulted in both laterally widened and longitudinally deepened breach. As the breach expanded, the sand-carrying capacity of the water flow increased, leading to a faster rate of failure. The breach process of overtopping can be categorized into four distinct stages: gully formation stage, lateral broadening stage of gully, cracks and collapse on the slope surface, and stable stage of collapse. (3) The tailings from the outflow spread downstream in a radial pattern, forming an alluvial fan. Additionally, the depth of the deposited mud first increased and subsequently declined as the distance from the breach grew. The findings of this research provide an important basis for the prevention and control of tailings dam breach disasters due to overtopping.

The dry beach length determines the hydraulic boundary of tailings impoundments and significantly impacts the infiltration line, which is crucial for the tailings dam. A deep learning method utilizing satellite images is presented to recognize the dry beach area and accurately measure the length of dry beaches in tailing ponds. Firstly, satellite images of various tailing ponds were gathered and the collection was enlarged to create a dataset of satellite images of tailing ponds. Then, a deep learning method was created using YOLOv5-seg to identify the dry beach area of tailing ponds from satellite images. The mask of the dry beach region was segmented and contour extraction was then carried out. Finally, the beach crest line was fitted based on the extracted contour. The pixel distance between the beach crest line and the dry beach boundary was measured and then translated into real distance by ground resolution. This paper’s case study compared the calculated length of dry beach with the real length obtained by field monitoring. The results of the case study showed that the minimum error of the method was 2.10%, the maximum error was 3.46%, and the average error was 2.70%, indicating high precision for calculating dry beach length in tailing ponds.