The information value (IV) model is a conventional method for landslide susceptibility prediction (LSP). However, it is inconsistent with the actual situation to regard all conditioning factors as equally weighted in the modeling process. In view of this, this paper studied the optimization effect of different weight calculation methods for IV model. Xingshan County, a typical landslide-prone area located in Hubei Province, China, was taken as a case study. The procedure was as follows: First, six conditioning factors, including elevation, slope angle, aspect, curvature, distance to river, and distance to road, were selected to form an evaluation factor library for analyzing the landslide susceptibility. Then, the weight of factors was calculated by fuzzy analytical hierarchy process (FAHP) and principal component analysis (PCA). On this basis, combined with the IV model, two weighted IV models (FAHP-IV model and PCA-IV model) were formed for LSP. The results shows that the optimization effect of PCA was the best. Moreover, compared with the IV-only model (AUC = 0.71), the FAHP-IV model (AUC = 0.76) and PCA-IV model (AUC = 0.79) performed better. The outcome also provided a feasible way for the study of regional LSP.

The settlement and deformation of an open-pit mine waste dump were investigated by using field monitoring and numerical methods. The creep parameters of fine soil sand in the dump fill were inverted using the steady Burgers creep model, and FLAC3D software was used to simulate the dumping soil construction and development program. The results show that the settlement displacement of the dump increases with the time of dumping soil and tends to be stable after 4 years. In the creep attenuation stage of the creep process, there is a big difference between the numerical calculation and the field monitoring results in the inversion process of fine-grained soil sand parameters. In the steady-state creep stage, the numerical calculation is consistent with the field monitoring value. The height of the fill has an influence on the settlement of the fill body of the dump and the time to reach the stability. The higher the filling height is, the greater the postconstruction settlement will be, and the longer it will take to reach stability. The pushing position of the fill body has a great influence on the settlement displacement of the dump. The time and efficiency of soil discharge can be shortened by optimizing the safe distance between the filling body and the river. Based on the numerical calculation results and the empirical settlement function, an analytical method for river channel location selection of internal dump is proposed.

At present, the research on goaf at home and abroad mainly focuses on four aspects: detection technology, stability evaluation technology, governance technology and quality control technology. The most important of the above four aspects is goaf detection technology. In order to ensure the accuracy and precision of exploration, many geophysical methods and high-density geological drilling are usually used for exploration. In case of complex terrain, this method will increase the workload rapidly, and can not achieve a good balance between exploration cost and exploration quality. Goaf exploration methods are still in the development stage, and each geophysical exploration method has its limitations. This study makes full use of the existing detection technology to detect the complex mined-out area of East Open-pit Mine, 9 inferred mined-out areas and 9 suspected mined-out areas were found by using 3D seismic exploration method, transient electromagnetic method is used to delineate 223 abnormal areas at different elevations within the exploration range. 58 drilling holes are arranged in the suspected mined-out area of East Open-pit Mine. Combined with geological software, 3D model map of mined-out area is drawn, and the causes of formation of mined-out area are classified and analyzed. Using 3D laser scanning technology to study the visualization of hidden mined-out areas, the hidden mined-out areas are divided into three types through visualization research, and its formation mechanism is analyzed. It can be applied to detection of open-pit mines which have small underground coal mines and many mined-out areas with complex geometric shapes and has great significance to the proposal of stability treatment scheme of mined-out area. The novelty of this study is prove the area, shape, roof thickness and height of the mined-out area by using joint detection method and the hidden mined-out area is visualized by 3D laser scanning technology.

As an important link in the complex system engineering project of open-pit mining, the quality of boundary determine the performance of the project to a large extent. However, the traditional design method cannot effectively measure the impact of uncertainties on the realm optimisation process. In this article, a coal price time series forecasting model that considers the amount of redundancy is proposed, which combines an improved sparrow search algorithm (ISSA) and a least squares support vector regression machine regression algorithm (LSSVR). The optimal values of the penalty factor and kernel function parameter of the LSSVR model are selected by ISSA, which improves the prediction accuracy and generalisation performance of the forecasting model. A multi-step decision optimisation method under fluctuating coal price conditions is proposed, and the model prediction results are applied to the boundary optimisation design process. Using the widely applied block model as the basis, a set of optimal production nested pits is obtained and only obtained, allowing the realm design results to fit the coal price fluctuation trend and further enhance the enterprise efficiency. The applicability and effectiveness of the proposed method is verified using an ideal two-dimensional inclined coal seamopencast mine model as an example.