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As one of the largest coal-rich provinces in China, Shanxi has extensive underground coal-mining operations. These operations have caused numerous ground cracks and substantial environmental damage. To study the main geological and mining factors influencing mining-related ground cracks in Shanxi, a detailed investigation was conducted on 13 mining-induced surface cracks in Shanxi. Based on the results, the degrees of damage at the study sites were empirically classified into serious, moderate, and minor, and the influential geological and mining factors (e.g., proportions of loess and sandstone in the mining depth, ratio of rock thickness to mining thickness, and ground slope) were discussed. According to the analysis results, three factors (proportion of loess, ratio of rock thickness to mining thickness, and ground slope) play a decisive role in ground cracks and can be respectively considered as the critical material, mechanical, and geometric conditions for the occurrence of mining surface disasters. Together, these three factors have a strong influence on the occurrence of serious discontinuous ground deformation. The results can be applied to help prevent and control ground damage caused by coal mining. The findings also provide a direct reference for predicting and eliminating hidden ground hazards in mining areas.

These studies indicate that natural disasters have been systematically monitored from three aspects: space, sky and wireless sensor networks. Furthermore, land subsidence caused by coal mining has been systematically investigated using remote sensing. However, the assessment of ground steps, cracks, and other forms of damage caused by underground mining still depends on ground monitoring methods, including field investigation, statistical analysis, and conventional measurement. Therefore, this study used field survey data along with the geological mining conditions to analyze the surface damage caused by underground mining in Shanxi Province.

We investigated 13 ground cracks caused by underground coal mining in Shanxi Province. Figure 1 shows the locations of the study sites. The specific location, size, mining depth, mining date, backfill collapse, and water filling condition of the extracted area and roadway were determined for each site by information collection and investigation. The geological conditions mainly included the position, shape, size, depth, and extension direction of the ground collapse pit; the relation of the ground step or crack to the goaf; the geologic structure; the mining boundary; and the advancing direction.

Based on the investigation of 13 ground cracks caused by underground coal mining in Shanxi Province, the ground damage grade along with the geological and mining conditions of the disaster sites are listed in Table 1.

Ground damage grade: Since there is no uniform standard for the level of ground damage caused by mining, damage in this study was classified according to the crack width and step size according to the grades defined in Table 2, which are based on empirical data from the land reclamation industry in China.

Loess and bedrock are two very different media with different physical and mechanical properties. The common types of mining surface failure in loess hilly mining areas are cracks, landslides, collapse pits, and caving, as shown in Fig. 5. Among these failure types, fracture is the most common. The type of failure is affected by the strata, topography, loess properties, mining conditions, and other factors. This study mainly analyzes the classification, formation mechanism, and influencing factors of mining-related fractures.

The crack is classified as a dynamic crack or permanent crack according to the timeframe of crack development. Dynamic surface cracks are mainly distributed directly above the coal face with the fracture direction perpendicular to the driving direction of the working face. The spacing of cracks is related to the periodic pressure of the roof of the working face. Some dynamic cracks will be closed or reduced as the working face advances. Permanent cracks are mainly distributed above the working face boundary, and their sizes and shapes tend to be stable after mining stops. Fracture development can be divided into four stages: the continuous deformation stage (early stage of mining); the generation and slow development stage; the intense development stage; and the stable fracture stage.

According to the crack characteristics, cracks are divided into tensile cracks and step cracks. In loess hilly mining areas, both tensile and step cracks will form. Faces with relatively large ratios of mining depth to mining thickness will develop mostly tensile cracks, while step cracks will form under conditions of thick soil with thin bedrock.

According to the formation mechanism, the cracks are divided into horizontal tensile fractures and vertical shear fractures. The change in overburden structure resulting from mining causes horizontal tension on the surface, while vertical shear occurs due to the movement of overburden in the vertical direction.

Another classification system divides the surface cracks caused by coal mining into four types: tensile, extrusion, collapse, crack, and sliding cracks. Extrusion cracks mostly occur in valley areas, while sliding cracks are primarily found on slopes covered by loess. The main factors controlling fracture development in coal mine goaf are bedrock thickness, loess overburden thickness, mining height, gully cutting topography (especially in mining areas with thick soil, thin bedrock, and large mining thickness), and slope.

Underground coal mining causes the destruction of surface land. Through a detailed investigation of ground cracks in 13 mines of six major coal fields in Shanxi Province, China, the main factors influencing ground surface damage were evaluated. Based on the analysis of investigation results, the conclusions can be summarized as follows.

In the coal mining process, the destabilization of loaded coal mass is a prerequisite for coal and rock dynamic disaster, and surface cracks of the coal and rock mass are important indicators, reflecting the current state of the coal body. The detection of surface cracks in the coal body plays an important role in coal mine safety monitoring. In this paper, a method for detecting the surface cracks of loaded coal by a vibration failure process is proposed based on the characteristics of the surface cracks of coal and support vector machine (SVM). A large number of cracked images are obtained by establishing a vibration-induced failure test system and industrial camera. Histogram equalization and a hysteresis threshold algorithm were used to reduce the noise and emphasize the crack; then, 600 images and regions, including cracks and non-cracks, were manually labelled. In the crack feature extraction stage, eight features of the cracks are extracted to distinguish cracks from other objects. Finally, a crack identification model with an accuracy over 95% was trained by inputting the labelled sample images into the SVM classifier. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm and can effectively identify cracks on the surface of the coal and rock mass automatically.

The procedure of coal mining is bound to cause an internal stress response in the coal and rock mass, causing a local stress concentration or pressure relief and leading to instability and failure of the coal and rock mass. In the destabilization process, different stress states and stress levels will lead to different forms of coal and rock damage. The most direct manifestation of these failure modes is the production of cracks on the surface of the coal and rock mass. The accurate detection and analysis of these cracks can provide important guidance for preventing and controlling the destabilization of coal and rock and improving the safety of underground personnel. Accurate and timely detection of cracks in the front coal wall can effectively prevent coal-rock dynamic disaster in the production process of coal mines. Experienced workers often judge whether there is a possibility of coal and rock dynamic disasters through cracks on the coal wall. However, this method is often time-consuming and labour-intensive, and there is a certain degree of error and risk.

Cho et al. (2016) [7] investigated the effects of illumination and shooting distance on crack image recognition by examining cracks in images taken with a camera. Nashat et al. (2014) [8] proposed a pyramid automatic crack detection scheme. Zhang et al. (2014) [9] presented an automatic crack detection and classification methodology for subway tunnel safety monitoring. Abdel-Qader et al. (2016) [10] used the principal component principles (PCA) algorithm to extract cracks in concrete bridge decks for the purpose of automating inspection. Iyer et al. (2005) [11] presented a three-step method to identify and extract crack-like structures from pipe images whose contrast had been enhanced. Sinha et al. (2006) [12,13] developed a statistical filter for the detection of cracks in pipes and a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images. Talab et al. (2016) [14] presents a new approach to image processing for detecting cracks in images of concrete structures. Li et al. (2014) [15] proposed a method consisting of three parts for bridge crack inspection. For the crack detection in a large structure, Sun et al. (2014) [16] presented a novel multi-scale algorithm for non-destructive detection of multiple flaws and (2016) [17] proposed a sweeping window method in elastodynamics for detection of multiple flaws embedded. 2b1af7f3a8