Affiliations: [a] State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Taifeng St, Huainan, China
| [b] School of Civil Engineering and Architecture, Anhui University of Science and Technology, Taifeng St, Huainan, China
| [c]
AnHui Transport Consulting and Design Institute, Hefei, China
Correspondence:
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Corresponding author. Wenbin Tao, AnHui Transport Consulting and Design Institute, Hefei 230088, China. E-mail: 592871796@qq.com.
Abstract: In order to improve bearing capacity of rockbolts in deep-buried coal mine roadways, orthogonal tests were conducted to study influencing factors of rockbolt anchoring effect. Wavelet neural network model was introduced to predict the pull-out force of rockbolt. The activation and output functions of the wavelet neural network were improved, and the scaling and translation parameters were also modified by using the gradient descent method. These improvements enhanced the approximation rate of the wavelet neural network model, and solve the problem that the wavelet transform method is monotonous and difficult to adapt to the complex and variable engineering conditions. Research results illustrated that The value of the ultimate pull-out force is positively correlated with the strength of the specimen and pre-tension value of the specimen. According to the test results, the coal mine roadway support scheme was optimized, and the high prestress full-length anchoring rockbolt support technology was proposed. The effectiveness of research was verified through the engineering applications and in-situ monitoring results.
Keywords: Rockbolt, improved Wavelet Neural Network, high prestress, pull-out test, field performance
