During the production of open-pit mines, the stability of slopes can be affected by various factors such as structural surfaces, production blasting vibrations, and mining areas. In this study, the researchers focused on the slope of the open-pit mine at Yinshan and employed UAV mapping technology to conduct an on-site geological engineering investigation. Information on the yield, trace length, spacing, and density of the structural surface of the south slope was obtained. The researchers also carried out vibration blasting tests in combination with the production blasting activities in the mine to determine the blasting vibration attenuation law and whether the blasting vibration speed met safety specifications. Additionally, numerical simulation methods were used to examine the influence of the mining area on the stability of the current slope and the designed excavation slope. The slope stability was evaluated using the limit equilibrium method, and the researchers separately discussed the influence of self-weight load and self-weight load plus blasting vibration force on the stability of the high slope of the open pit. The results showed the following: (1) The rock mass structural plane in the south slope of the mining area was mainly dominated by a medium-large dip structural plane, and three faults and joint fissures in the investigation area combined to form cutting and sliding surfaces in the rock mass that were prone to collapse and sliding. (2) The maximum blasting vibration speed met safety requirements. (3) There was no large range of plastic zone damage in the entire slope, and the overall stability of the slope was good. (4) The present slope was relatively stable when considering only self-weight stress and the blasting vibration force. However, there was a certain risk of instability in the design of the excavation slope.

Rockbursts are common geological disasters in underground engineering, and rockburst proneness evaluation is an important research subject. In this study, a multidimensional cloud model was used to evaluate the rockburst proneness level, and the control variable method was used to establish 15 multidimensional cloud (MC) models. The key factors affecting the accuracy of multidimensional cloud model evaluation are numerical characteristics, weight, and normalization methods. The optimal numerical characteristics calculation method of a multidimensional cloud model was determined, and an improved CRITIC (IC) weight method was optimised by introducing the relative standard deviation and an improved quantisation coefficient. Six rockburst indexes were used as input for the multidimensional cloud model, including the elastic deformation energy index $Wet$⁠, maximum tangential stress $σθMPa$ of a cavern, uniaxial compressive strength $σcMPa$⁠, uniaxial tensile strength $στMPa$⁠, strength brittleness coefficient $B1=σc/στ$⁠, and stress coefficient $σθ/σc$⁠. The model was used to learn 271 groups of complete rockburst cases, and the MC-IC rockburst proneness evaluation method was established. The performance of the proposed MC-IC rockburst proneness method was verified by an 8-fold cross-validation and confusion matrix (precision, recall, F₁). The method was tested to evaluate 20 groups of rockburst cases from the Jiangbian Hydropower Station, and the accuracy reaches 95%. The evaluation results were compared with three empirical criteria: four cloud-based methods, three unsupervised learning methods, and four supervised learning methods; the accuracy of the method established in this paper is 93.33%. The results showed that the MC-IC method had an excellent performance in evaluating rockburst proneness and can provide a practical basis for identifying rockburst hazard areas in deep engineering.