Abstract
In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.
Highlights
China has applied anchor rods to coal mine tunnels since 1956
The bolt support design is too conservative; on the other hand, the roof accidents of the bolt support coal roadway occur frequently [4,5,6], which becomes the restrictive factor for the widespread application of the bolt support technology in the coal roadway
According to the statistics of coal mine accidents in the country from 2007 to 2016, the number of roof accidents accounts for about 50% of the total coal mine accidents, and the death toll of roof accidents accounts for 35.5% [7]
Summary
China has applied anchor rods to coal mine tunnels since 1956. After several technical breakthroughs, bolt support has become the main support structure of coal mine tunnels at present [1,2,3]. BP neural network has a high modeling ability To this end, the paper uses BP neural network to classify the roof stability of coal roadway supported by bolts in Shendong mining area. After determining the classification index value method and the type of roof stability of coal roadway supported by bolts in the mining area, it is necessary to determine a reasonable evaluation factor for various stable roof types before the classification model is established. E basis for determining the evaluation factors of each stability type in this paper is based on the previous theoretical research results [9], combined with the actual maintenance status of coal roadway roof in Shendong mining area and the case analysis results of a large number of bolts supporting coal roadway roof fall in the mining area. Erefore, the evaluation factors of all classification indexes and their corresponding roof stability types are determined. e results are shown in Table 2 (original data) and Table 3 (standardized data)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
