In view of the effect of water on the physical and mechanical parameters of natural gypsum rock, in this study, gypsum rock in the goaf of the gypsum mine in the Jiangsu Province was selected as the research object, and gypsum rock samples were prepared with different immersion times. In addition, uniaxially tests were performed separately on the gypsum rock samples. Compression and scanning electron microscopy experiments were carried out to analyze the effects of immersion time on the uniaxial compressive strength, elastic modulus, Poisson’s ratio, and microstructure of gypsum. The results show that the uniaxial compressive strength and elastic modulus of gypsum are inversely proportional to the water content. However, the Poisson’s ratio is direly proportional to the water content, and the failure mode is destroyed by the brittle fracture of a single crack and is transformed into the shear ductile failure of the Y-shaped crack. Microscopically, with increasing immersion time, the bonds in the crystal of the microporous cracks and microcrack tips are weakened by hydrolysis, and the macroscopic structure is complicated by the internal structure of gypsum, and the end of the crack is expanded by the compressive action of water. Based on the damage mechanics, the evolution equation of gypsum soaking softening damage based on time factor was derived. The relationship between the brittleness coefficient and softening damage variable is revealed, providing a theoretical basis for the determination of the softening degree of gypsum in the goaf.

Residual surface deformation resulting from abandoned mined-out areas can lead to severe damage to ground structures (e.g., buildings and infrastructure in mining areas) and the local ecological environment. Long-term monitoring and analyses of surface deformation characteristics of abandoned mined-out areas are significant for preventing potential disasters. In this study, a detailed field investigation first was conducted in Ying’an and Baoshan coal mines located in Jilin Province, China, to survey mining-induced disasters in the mining areas. Based on the 40 Sentinel-1A images acquired from 14 February 2017 to 17 May 2020, small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) technology was employed to obtain the time-series residual surface deformation. Validation of the SBAS-derived results is performed by comparing with the results obtained via leveling measurements. The root mean square error (RMSE) between SBAS-derived and leveling measurements results was found to be 1.144 mm, reflecting a fairly good agreement. Furthermore, the ordinary Kriging interpolation approach was adopted to obtain information on the deformation across the entire area. The spatial–temporal evolution characteristics of the derived subsidence bowls in multiple mined-out areas were revealed. The deformation characteristics for the abandoned mined-out areas in different periods were not completely consistent. Finally, the potential mechanism underlying the inconsistency in the subsidence associated with underground coal exploitation is analyzed. The findings of this study can provide insights into local construction and ecological improvement as well as guidance for the prediction of deformation in abandoned mined-out areas.

Debris flow susceptibility analysis is a prerequisite of risk assessment. The main objective of this study was to explore the accuracy and practicability of mapping units for evaluation of debris flow susceptibility. These units include grid cell units (GCUs), and watershed units (WUs) with the flow thresholds 10 000 (WU 10 000) and 5000 (WU 5000). The frequency ratio (FR) model was selected as the statistical method. Yongji County (YJC) of Jilin Province, China was selected as the research site, and a total of 123 debris flow disasters were surveyed. Eight influencing factors were considered and a total of three models were constructed. The predictive capabilities of the models were verified using an ROC curve and AUC. The results showed the three models to be accurate and the evaluation results of the GCU were found to be more accurate than others. However, when considering the effects of geology and geomorphology on the occurrence of debris flows, the WU was more feasible than the GCU. Therefore, the results indicate that the evaluation of debris flow susceptibility should be carried out based on the WU of the appropriate flow threshold in combination with the actual prevention and control of debris flow disasters.

Landslides and collapses are common geological hazards in mountainous areas, posing significant threats to the lives and property of residents. Therefore, early identification of disasters is of great significance for disaster prevention. In this study, we used Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to process C-band Sentinel-1A images to monitor the surface deformation from Songpinggou to Feihong in Maoxian County, Sichuan Province. Visibility analysis was used to remove the influence of geometric distortion on the SAR images and retain deformation information in the visible area. Hot spot and kernel density analyses were performed on the deformation data, and 18 deformation clusters were obtained. Velocity and slope data were integrated, and 26 disaster areas were interpreted from the 18 deformation clusters, including 20 potential landslides and 6 potential collapses. A detailed field investigation indicated that potential landslides No. 6 and No. 8 had developed cracks and were severely damaged, with a high probability of occurrence. Potential collapse No. 22 had developed fissures, exposing a dangerous rock mass and posing significant threats to the lives and property of residents. This study shows that the proposed method that combines visibility analysis, InSAR deformation rates, and spatial analysis can quickly and accurately identify potential geological disasters and provide guidance for local disaster prevention and mitigation.

Gravel cushions are widely used to absorb the impact energy of falling rocks in open-pit mines. A particularly important application is to enhance the energy-absorbing capacity of rockfall sheds. In this paper, we study how varying the thickness and particle size of a gravel cushion influences its energy-consumption and buffering effects. We performed a series of laboratory drop tests by dropping blocks from a fixed height onto cushions of different thicknesses and particle sizes. The results indicate that, for a given impact energy, the cushion thickness has a strong influence on the measured coefficient of restitution (COR) and therefore impact pressure. Additional tests were performed to study how the radius of the block and the height it is dropped from affect the measured COR. This showed that as the movement height of the block is increased the COR also increases, and blocks with larger radii exhibit a larger variability in measured COR. Finally, we investigated the influence of rockfall block radius, r, movement height, H, cushion thickness, h, and particle size, d, on the COR and the damage depth, L, of the cushion. The test results reveal that the cushion thickness is the primary design parameter, controlling not only COR, but also the stability of the cushion material. The results provide a theoretical and practical basis for the design of gravel cushions for rockfall protection.

Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.