Failure analysis and prediction of roof instability in end face under repeated mining using early warning system

Abstract

The overlying strata of the lower coal seam is easy to be collapsed causing the roof caving accident at the end face of the mining working face under repeated mining in close-distance coal seams. In order to predict the roof instability of the end face, the mechanical model of the granular arch structure is established in this study to further analyze its main influencing factors. The results show that the mining height of the working face, the advancing speed, the distance of coal seams, the tip-to-face distance, the strength of the surrounding rock and the support setting the load of the support are the main influencing factors on the roof caving of the end face. Subsequently, the prediction model of roof instability in the end face under repeated mining is constructed through the radial basis function neural network (RBFNN) and the above main influencing factors are regarded as input layer indexes. Meanwhile, the roof subsidence, coal wall deformation and support load are determined as the output layer indexes. The predicted results are closer to the results of sample tests. Finally, the early warning system, including monitoring and early warning, data query, emergency management, user management, and system settings, is designed to monitor roof conditions of the end face and timely warn the roof accidents. The field application proves that the system has good practical value, which is of great significance to intelligent prediction of coal mine stope disaster and prevent the end face roof disaster under repeated mining and. This will promote the safe and efficient construction of coal mine production.

Gas explosion early warning method in coal mines by intelligent mining system and multivariate data analysis

“…Li et al (2023) used Radial Basis Function (RBF) neural network to build a prediction model of roof instability under repeated mining. Compared with the general RBF neural network prediction model, the correlation coefficient between the predicted value of the research model and the actual value of the output index was greater than 0.05 [ 12 ]. Thilagavathi et al (2023) used the comprehensive activities of temperature, tension and gas sensors and the IoT module to identify the temperature, strain and climate in the coal mine shaft, and recorded all the information into the cloud by using the information log to establish a coal mine safety monitoring system [ 13 ].…”

Characteristics of deformation and damage of surrounding rock along the top roadway in the working face of an isolated island and its evolution law

“…The redistribution of surrounding rock stress due to mining disturbances is a key factor contributing to plastic failure zones in roadways 5 , 6 . Such disturbances can also trigger dynamic disasters such as rib spalling, roof falls, and even rock bursts 7 – 10 . Current research into the failure characteristics and spatial extent of surrounding rock in mining roadways includes theories such as the natural caving arch 11 , maximum horizontal stress theory, and axial strain theory 12 , 13 , supported by both theoretical analyses and field engineering practices.…”

Study on destabilization failure characteristics and energy evolution law of drilling holes

“…The fracture, strain, and energy evolution characteristics of the specimens under load are helpful to summarize the causes of borehole destabilization damage, but most of the above studies on the fracture, strain, and energy evolution characteristics of the specimens during the compression experiments were conducted on the pore‐containing specimens made of similar materials or with a single pore diameter 23–30 . The fracture, strain, and energy evolution characteristics of pore‐bearing specimens during loading can show large differences as the pore size increases.…”

“…The fracture, strain, and energy evolution characteristics of the specimens under load are helpful to summarize the causes of borehole destabilization damage, but most of the above studies on the fracture, strain, and energy evolution characteristics of the specimens during the compression experiments were conducted on the pore-containing specimens made of similar materials or with a single pore diameter. [23][24][25][26][27][28][29][30] The fracture, strain, and energy evolution characteristics of pore-bearing specimens during loading can show large differences as the pore size increases. Therefore, to find out the fracture development pattern, strain, and energy evolution characteristics of pore-bearing rock specimens with different pore diameters under load, uniaxial compression experiments were conducted on pore-bearing rock specimens, and the fracture and strain evolution characteristics of specimens under load were monitored based on digital image correlation (DIC) technology to summarize the causes of instability and damage of porebearing rock specimens with different pore diameters, and to provide some theoretical guidance for gas extraction drilling stability research.…”