China University of Mining and Technology (CUMT)

Xuzhou Hongyi Technology Development Co., Ltd.

Xuzhou Wushou Information Technology Co., Ltd.

Yujie Peng

Anye Cao

Yaoqi Liu

Xu Yang

Geng Li

Changbin Wang

Chengchun Xue

Xianxi Bai

Zhifeng Ma

Songwei Wang

Jianqiu Xue

Guowei Lyu

Yingyuan Wen

Guowei Lv

Wenhao Guo

Fan Chen

A rock burst hazard prediction method based on vibration wave energy attenuation characteristics of a mine earthquake cluster is provided. The rock burst hazard prediction method comprehensively considers the static load and dynamic load effects of the vibration waves of the mine earthquake cluster based on vibration wave energy attenuation characteristics of the mine earthquake cluster. The static load strength index and the dynamic load strength index involved in the method have clear physical meanings. A comprehensive prediction index calculation model proposed based on the dynamic and static load superposition principle of rock burst occurrence is clear, and the method has a firm theoretical support as well as strong universality and operability. Meanwhile, the updating and adjustment of weights are rapid and the objective judgment and prediction of the final comprehensive prediction results are efficient, and the high-energy mine earthquake and impact behavior area can be effectively predicted.

Rock burst hazard prediction method based on seismic wave energy attenuation characteristics of mine earthquake cluster

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

Method for assessing stability of roadway surrounding rock based on numerical simulation and deep learning

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Xuzhou, China

Yujie Peng