Collapsibility is a unique engineering geological property of loess. Choosing appropriate parameters to build the prediction model of loess collapsibility is an essential step toward solving the loess collapsibility problem. A case study was performed for the loess in Xinyuan County of the Yili River Basin, China. A large amount of data was collected from preliminary geotechnical tests in this region. Mathematical statistics were applied to analyse the correlations between the loess collapsibility and soil parameters. Multiple linear regression and neural network theories were adopted to build this region’s prediction model of loess collapsibility. The results showed that microscopically, the soils in this region were predominantly flocculated structures. The soil particles were flaky and in bracket contact, and the pores were round or irregularly shaped. Regarding the material composition, the soils were primarily composed of quartz and albite, with a low hematite content. In the study area, the correlation coefficients between the collapsibility coefficient of the loess vs. the density, dry density, saturation, porosity ratio, and porosity varied between 0.628 and 0.857, indicating a strong or very strong correlation. In terms of predicting loess collapsibility, the effectiveness of neural networks based on RBF (radial basis function) and multiple linear regression models was contrasted. The latter was discovered to be more appropriate, dependable, and accurate, with an accuracy percentage of 94.42%. Simultaneously, the model’s assessment index is 0.014 for the root mean squared error (RMSE), 0.962 for the correlation coefficient (CC), 0.919 for the Nash–Sutcliffe efficiency coefficient (NSE), and −1.494 percent for the percent bias (PBIAS). It works well for estimating whether local loess may collapse. Therefore, the RBF neural network model built in the present study has adequate precision and meets the engineering requirements. Our research sheds new light on loess collapsibility assessment in this region.

With the increasing improvement of national ecological standards, the eco-environmental problems caused by super thick coal seam mining in western China are becoming more and more serious. The failure law of weak overburden stratum is an important factor affecting the safe mining of coal. The failure characteristics of weakly cemented overburden under high-intensity mining in the mining area of western China were studied. For this purpose, a case study was conducted in the 1101 working face of the Baituyao Coal Mine in Ürümqi County. Based on the analysis of geological conditions in the study area, we combined empirical calculations with engineering analogy, physical simulation, and numerical simulation to comprehensively analyze the characteristics of mining-induced overburden failure. The study showed that the overburden in the study area had several unfavorable engineering geological characteristics, including ease of softening in the presence of water. The Middle Jurassic Xishanyao Formation is a directly recharged aquifer with a weak water-retaining property. Overburden failure mainly occurred at the two ends of the open-off cut. During the mining process, vertical fissures and bed-separated fissures were periodically developed and closed, and the fissures were interconnected. The overburden was fractured, and the fractured zone showed a trapezoidal shape, tapering off from bottom to top. The heights of the caving zone and the water-conducting fracture zone were 25 and 280 m, respectively, in the 1101 working face of the Baituyao Coal Mine, and the ratio of fracturing to mining height was 14.0. Due to the weakly cemented overburden and the presence of the Neogene weak aquifer, the risk of water and sand bursts still exists in this working face under high-intensity mining. Our findings shed light on the safe mining and environmental protection of the ground surface in coal mine shafts in western China.