Effects on Buildings of Surface Curvature Caused by Underground Coal Mining

Ground curvature caused by underground mining is one of the most obvious deformation quantities in buildings. To study the influence of surface curvature on buildings and predict the movement and deformation of buildings caused by ground curvature, a prediction model of the influence function on mining subsidence was used to establish the relationship between surface curvature and wall deformation. The prediction model of wall deformation was then established and the surface curvature was obtained from mining subsidence prediction software. Five prediction lines were set up in the wall from bottom to top and the predicted deformation of each line was used to calculate the crack positions in the wall. Thus, the crack prediction model was obtained. The model was verified by a case study from a coalmine in Shanxi, China. The results show that when the ground curvature is positive, the crack in the wall is shaped like a "V"; when the ground curvature is negative, the crack is shaped like a "∧". The conclusion provides the basis for a damage evaluation method for buildings in coalmine areas

Law of Movement of Discontinuous Deformation of Strata and Ground with a Thick Loess Layer and Thin Bedrock in Long Wall Mining

The surface discontinuous deformation caused by coal mining has great damage to the ecological environment and threatens the safety of human lives. Focusing on the problem of discontinuous deformation (ground fissures and collapsed pits) in mining areas with a thick loess and thin bedrock, this paper uses a coal panel in southern Shanxi in China as research background, and uses field investigation, theoretical analysis and the particle flow code 2D (PFC2D) numerical simulation method to study the movement of overburden and discontinuous ground deformation of mining areas with a thick loess layer and a thin bedrock. The results show that with the continual advance of the working face, the failure of the overlying rock, the changing of force chain shape and the development of cracks under this geological and mining condition have their unique rules. This study analyzes the law of movement of overburden in coal seam mining, explains why discontinuous deformation of the surface occurs in case of a thick loess layer and thin bedrock, and provides reference for the prediction of fracture development under the same geological conditions and the application of the PFC2D in coal seam mining in different geological conditions

Physical Experiments on the Deformation of Strata with Different Properties Induced by Underground Mining

Underground mining can cause ground and strata movements, which in turn cause damage to houses and the landscape. The different characteristics and properties of the strata encountered during mining can also result in corresponding deformation. In order to study the deformation and damage rules of strata which are composed of unconsolidated soil and bedrock induced by underground coal mining, a physical model that employs material sand, lime, and gypsum with water was utilized firstly to simulate strata and ground movements. Then overlying strata with different properties were created according to the corresponding ratio of the mixed material, physical models under two conditions (i.e., thick soil layer and thin bedrock, and thin soil layer and thick bedrock) were set up. Lastly underground coal extraction was conducted using the proposed models. Results show that the proportion of unconsolidated soil layer in the overlying strata is the key factor that determines the significant differences in the movement of strata under the two special conditions. When the ratio of the soil layer is large, the unconsolidated soil layer is loaded on the bedrock; the bedrock is thus forced to move down, and the compression rate of the broken strata is increased. The soil layer follows the bedrock as an integral movement to subsidence. When the ratio of the soil layer is small, the load on the strata is small, but the structural function of the strata is obvious and the fraction degree in the strata is developed. The obtained results in this study can be applied to support mine planning in the aspect of ground damage evaluation

Non-Measuring Camera Monitoring of Comprehensive Displacement of Simulated Slope Mass Based on Edge Extraction of Subpixel Ring Mark

Slope hazards threaten the safety of buildings and people’s lives and property. Real-time and dynamic monitoring of slope deformation by digital image monitoring technology is an effective method to prevent slope hazards. In this study, the Zhang Zhengyou calibration method is used to calibrate a non-metric digital camera, which is used to monitor the simulated slope with ring marks. The sub-pixel algorithm is used to identify the center coordinates of the landmarks. The proportional coefficient is obtained from the relationship between the landmarks and the actual distance. The change in displacement of the position of the digital camera is calculated in combination with the specific displacement value of the slope, yielding the rapid and accurate displacement trend of the slope. The outdoor experimental results show that the monitoring accuracy of this method can reach millimeter level, which can meet the demand of slope monitoring

Monitoring Mining Surface Subsidence with Multi-Temporal Three-Dimensional Unmanned Aerial Vehicle Point Cloud

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides

Study on the Development Law of Mining-Induced Ground Cracks under Gully Terrain

Coal seam mining in the gully area easily causes ground cracks and even induces landslides, which endanger the safety of mining areas. In this paper, combined with the mining conditions of a mining area in southern Shanxi Province, China, ground crack mapping, crack width dynamic monitoring, and the numerical simulation method are used to study the static and dynamic evolution law and the formation mechanism of ground cracks in the gully area. The research shows that ground cracks mainly include dynamic in-plane cracks and boundary cracks. The dynamic in-plane cracks show the characteristics of “opening first and closing later”. The boundary cracks show the characteristics of “only opening and not closing”. It is found that the closure of the dynamic in-plane cracks will decrease (compared with plain areas). The development of ground cracks experiences three stages: the initial formation stage, the dynamic development stage, and the gradually stable stage. The “goaf–surface” structure model and force chain arch structure model are established to more intuitively analyze the formation mechanism of ground cracks. The research results have a specific reference value for preventing ground disasters caused by underground coal mining and land ecological restoration

Research on Identification and Location of Mining Landslide in Mining Area Based on Improved YOLO Algorithm

The wide range and high intensity of landslides in the mining area pose a great threat to the safety of human life and property. It is particularly important to identify and monitor them. However, due to the serious surface damage, small landslide scale, complex background and other factors in the mining area, it is impossible to accurately identify and detect the landslide in the mining area. It is necessary to select an efficient detection model to detect it. In this paper, aiming at the problem of landslide identification in mining area, the remote sensing image of mining area is obtained by unmanned aerial vehicle (UAV), and the landslide data set of mining area is constructed by data enhancement method. An improved YOLOv8 algorithm is proposed. By adding a mixed attention mechanism in the channel and spatial dimensions, the detection accuracy of the model for mining landslide is improved, and the monitoring of landslide changes in the mining area is successfully completed. At the same time, an algorithm for locating the landslide position is proposed. Through this algorithm, the detected landslide pixel coordinates can be converted into geodetic coordinates. The results show that the improved YOLOv8 algorithm proposed in this paper has a recognition accuracy of 93.10% for mining area landslides. Compared with the [email protected] of the original YOLOv8 algorithm and YOLOv5 algorithm, the improved YOLOv8 algorithm has an increase of 4.2% and 5.1%. This study has realized the monitoring and positioning of the landslide in the mining area, which can provide the necessary data support for the ecological restoration on mining area

DInSAR Monitoring of Surface Subsidence by Fusing Sentinel-1A and -1B Data to Improve Time Resolution in a Mining Area

Monitoring large gradient ground deformation due to temporal and spatial image decoherence has long been a challenge. We attempted an improvement using Sentinel-1A and Sentinel-1B C-band data fusion methods based on the variation law of subsidence velocity of ground leveling monitoring points. This approach improved the temporal resolution from 12 to 6 days. Using a mine in Datong, Shanxi Province, China as an example, 13 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were fused and compared with ground-measured data. The results obtained were closer to the measured values than those obtained by single data set (Sentinel-1A or -1B only). Simultaneously, 61 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were used to calculate the subsidence of the mining area over two years. The subsidence map was consistent with the actual leveling trend, which reflected the dynamic change of surface subsidence range in the mining area. This study provides an approach using DInSAR technology to monitor large gradient deformation in mining areas and provides an effective method to monitor of surface dynamic deformation

Accuracy Assessment of a UAV Direct Georeferencing Method and Impact of the Configuration of Ground Control Points

Unmanned aerial vehicles (UAVs) can obtain high-resolution topography data flexibly and efficiently at low cost. However, the georeferencing process involves the use of ground control points (GCPs), which limits time and cost effectiveness. Direct georeferencing, using onboard positioning sensors, can significantly improve work efficiency. The purpose of this study was to evaluate the accuracy of the Global Navigation Satellite System (GNSS)-assisted UAV direct georeferencing method and the influence of the number and distribution of GCPs. A FEIMA D2000 UAV was used to collect data, and several photogrammetric projects were established. Among them, the number and distribution of GCPs used in the bundle adjustment (BA) process were varied. Two parameters were considered when evaluating the different projects: the ground-measured checkpoints (CPs) root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2) distance. The results show that the vertical and horizontal RMSE of the direct georeferencing were 0.087 and 0.041 m, respectively. As the number of GCPs increased, the RMSE gradually decreased until a specific GCP density was reached. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain. Additionally, as the distance to the nearest GCP increased, the local accuracy of the DSM decreased. In general, UAV direct georeferencing has an acceptable positional accuracy level