The Rock Burst Hazard Evaluation Using Statistical Learning Approaches

Jie Chen,Xusheng Zhao,Like Wei,Bo Peng,Zhigang Zhang,Jingkuan Gao,Yuanyuan Pu,Mingzhong Gao,Chong Wang,Guangchao Zhang,Xiaowei Feng

doi:10.1155/2021/5576480

Abstract

The great threat and destructiveness brought by a rock burst make its prediction and prevention crucial in engineering. The rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control factors and discrimination conditions of rock burst were accepted by conventional risk determination methods, the rock burst risk determination in the same area may produce conflicting results. In this study, Naive Bayes statistical learning models based on different model prior distributions representing highly complicated nonlinear relationship between rock burst hazard and impact factors were built to evaluate the rock burst hazards. The results suggested that the Bayes statistical learning model based on a Gaussian prior has the strongest performance over four preset prior distributions. Combining the rock mechanics parameters measured in the laboratory and the stress data collected on the project sites, the proposed model was successfully employed to evaluate the kimberlite rock burst risk of a diamond mine in Canada. The Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data.

Highlights

A rock burst is a kind of sudden and severe rock instability, referring to a dynamic geological disaster caused by the sudden release of elastic strain energy accumulated in the rock mass of the underground excavations [1, 2]
E comprehensive index methods mainly use mathematical and statistical models to carry out a weighted mapping of the control factors of rock burst and calculate a comprehensive value to judge the risk of rock burst. e common synthetic index methods to evaluate rock burst risk include principal component analysis [10], fuzzy mathematics [11], and analytic hierarchy process [12]
(1) is paper uses a statistical learning model to obtain information from the collected data of 111 rock burst cases and trains Naive Bayes classifiers based on different prior distributions

Summary

The Rock Burst Hazard Evaluation Using Statistical Learning Approaches

E rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control factors and discrimination conditions of rock burst were accepted by conventional risk determination methods, the rock burst risk determination in the same area may produce conflicting results. Naive Bayes statistical learning models based on different model prior distributions representing highly complicated nonlinear relationship between rock burst hazard and impact factors were built to evaluate the rock burst hazards. E results suggested that the Bayes statistical learning model based on a Gaussian prior has the strongest performance over four preset prior distributions. E Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data Combining the rock mechanics parameters measured in the laboratory and the stress data collected on the project sites, the proposed model was successfully employed to evaluate the kimberlite rock burst risk of a diamond mine in Canada. e Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data

Introduction

Case σθ

False positive rate

True positive rate

No Medium Strong Medium Medium Medium Medium Strong

Conclusion and Discussion