Ground fissures are generated during the coal mining process due to overlying strata migration, which provides gas and water seepage channels and usually contributes to coal mining accidents in shallow buried coal seams with larger mining height working faces. Thus, the evolution features of ground fissures in large mining height working faces and shallow buried coal seams were explored by considering field observation data, similar simulation, and numerical simulations. The results show that the weathered rock layer above the thin bedrock inhibits fracture growth caused by coal mining. Overlying strata fracture expansion can be quantitatively divided into three stages based on fractal dimension: the overburden fracture formation stage, the overburden deformation fracture expansion stage, and the overburden fracture stable development stage. The movement deformation region of the ground fissure can be described by three characteristic zones: the boundary tension zone, the central compression zone, and the central dynamic tension and compression zone. Central dynamic fissures usually have 3–5 fissure counts with 2–5 m intervals between each group of fissures; central fissures develop twice from open to closed widths; the time period for two central fissures to reach their with maximum widths is 11–20 days; and border fissures expand rapidly with maximum values in a time period of 5–6 days.

Hidden mining-induced fissures connected to a goaf may induce spontaneous combustion of abandoned coal, threatening safe coal mining operation and ecological and environmental protection. To identify hidden mining-induced fissures rapidly, accurately and in a timely manner, a novel method involving infrared remote sensing via an unmanned aerial vehicle (UAV) was proposed. Hidden mining-induced fissures above working face No. 52605 of the Daliuta coal mine were continuously monitored using this method. Field experiments revealed that hidden mining-induced fissures could be effectively identified via infrared technology. The diurnal variation in the hidden mining-induced fissure temperature was cosinusoidal. The temperature of the hidden mining-induced fissures was highly correlated with burial depth, and the burial depths of the identified hidden mining-induced fissures differed at various times. The temperature differences among hidden mining-induced fissures, aeolian sands and vegetation varied with time and burial depth. The temperature difference variation between in situ hidden mining-induced fissures and aeolian sand matches that between hidden mining-induced fissures at a 20 cm burial depth and sand. In situ hidden mining-induced fissures could be identified from 1:00 to 5:00 a.m. and from 11:00 a.m. to 7:00 p.m. under the studied conditions.

The geometric characteristics of these fractures have a substantial influence on the mechanical and energy properties of heterogeneous rocks. This study calibrated the experimental results using the finite-discrete element method (FDEM). An orthogonal design was employed to investigate the effects of the homogeneity coefficient, fracture angle, fracture length, and fracture aperture on the mechanical and energy characteristics of fractured sandstone. The main factors influencing the mechanical properties and energy characteristics of rocks were explored through multi-factor correlation analysis. The effects of fracture geometric features and heterogeneity on the mechanical properties and energy characteristics of rocks were analyzed by single-factor analysis. A regression model between peak stress and fracture geometric features was established. The results show the following: The homogeneity coefficient and fracture length have a significant impact on the elastic modulus of fractured sandstone. The fracture angle and fracture length have a significant influence on the peak strain, elastic strain energy and total energy of fractured sandstone. The fracture angle, fracture length and homogeneity coefficient have a significant effect on the peak stress of fractured sandstone. The elastic modulus and peak stress show a logarithmic relationship with the homogeneity coefficient, while the elastic strain energy and total energy have a logarithmic relationship with the crack length. The peak strain and peak stress have a quadratic polynomial relationship with the crack angle, and the elastic strain energy and total energy also have a quadratic polynomial relationship with the crack angle. The elastic modulus, peak strain, and peak stress have a logarithmic relationship with the crack length. The predicted values of peak stress and numerical calculation errors of fractured rocks mainly range from 0.07% to 7.76%, with an average error of 2.58%. Both the peak stress prediction values and the numerical calculation results show a “U”-shaped change trend, first decreasing and then increasing with the increase in the fracture angle. This study investigates the influence of fracture geometric characteristics on the mechanical and energy characteristics of heterogeneous rocks, which is of great significance for the stability control of fractured rock masses and the optimization of underground engineering parameters. The core challenge for future research lies in revealing the intrinsic connection among fracture geometric features, rock mass heterogeneity, and multi-field coupling effects to meet the complex engineering demands of deep mining, thereby serving the safe production and disaster prevention of deep mines.

It is important to accurately predict the height of the water conducting fracture zone (WCFZ) for the production safety of coal mines and ecological environmental protection. Through the field measurement data of WCFZ at working face in Shendong and neighboring mines. The correlation degree between the working face length, mining height, mining depth, coal seam dip angle, mining speed, hard rock proportionality coefficient and the height of WCFZ was analyzed by using gray relationship analysis theory. The BP neural network model optimized by genetic algorithm was trained, and the predicted WCFZ values of empirical formula, GA-BP and BP neural network model were compared with the actual measured values of WCFZ in the mine area. The comparison analysis showed that the GA-BP neural network model had the smallest error of 7.78% in the prediction value. Therefore, the GA-BP neural network model has sufficient prediction accuracy and generalizability and can be applied to study the WCFZ in the Shendong mine area.