期刊
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
卷 61, 期 -, 页码 61-70出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.tust.2016.09.010
关键词
Rock burst; Bayesian networks; Tree augmented Naive Bayes classifier; Incomplete data; Cross-validation; Sensitivity analysis; Maximum tangential stress
资金
- Spanish Ministry of Economy and Competitiveness [BIA 2015-69152-R]
- China Scholarship Council (CSC)
- Fundacion Jose Entrecanales Ibarra
Rock burst is a dynamic process of sudden, rapid and violent release of elastic energy accumulated in rock and coal masses during underground activities. It can lead to casualties, to failure and deformation of the supporting structures, and to damage of the equipment on site; hence its prediction is of great importance. This paper presents a novel application of Bayesian networks (BNs) to predict rock burst. Five parameters Buried depth of the tunnel (H), Maximum tangential stress of surrounding rock (MTS) (sigma(0)), Uniaxial tensile strength of rock (UTS) (sigma(t)), Uniaxial compressive strength of rock (UCS) (sigma(c)) and Elastic energy index (W-et) - are adopted to construct the BN with the Tree augmented Naive Bayes classifier structure. The Expectation Maximization algorithm is employed to learn from a data set of 135 rock burst case histories, whereas the belief updating is carried out by the Junction Tree algorithm. Finally, the model is validated with 8-fold cross-validation and with another new group of incomplete case histories that had not been employed during training of the BN. Results suggest that the error rate of the proposed BN is the lowest among the traditional criteria with capability to deal with incomplete data. In addition, a sensitivity analysis shows that MTS is the most influential parameter, which could be a guidance on the rock burst prediction in the future. (C) 2016 Elsevier Ltd. All rights reserved.
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