The Influence of Bayesian Networks Structure on Rock Burst Hazard Prediction with Incomplete Data. (2017)

• Record Type: Journal Article
• Authors: Li, Ning; Jimenez, R.; Feng, Xianda
• Is Part Of: Procedia engineering. Volume 191(2017)
• Page Start: 206
• Page End: 214
• Publication Date: 2017
• Subjects: Rock burst -- BN-Bayesian networks -- Naïve Bayes classifier -- Tree augmented Naïve Bayes classifier -- BN augmented Naïve Bayes classifier -- Incomplete data -- Cross-validation; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.05.173
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Rock burst is often induced by the superposition of static and dynamic loads that produces failure with a sudden and violent release of elastic energy accumulated in rock and coal masses during underground activities. Casualties, deformation of the supporting structures and damage of the equipment on site are some of its consequences, hence producing a need to study its prediction. A novel application of Bayesian networks (BNs) to predict rock burst is proposed in this paper. In order to analyze the influence of the network structure, several networks are constructed with five parameters: Tunnel depth (H), Maximum tangential stress of surrounding rock (MTS) (σθ ), Uniaxial tensile strength of rock (UTS) (σt ), Uniaxial compressive strength of rock (UCS) (σc ) and Elastic energy index (Wet ). The Expectation Maximization algorithm is employed to learn from a data set of 135 rock burst case histories with incomplete data, whereas belief updating is carried out by the Junction Tree algorithm. 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, and the influence of the network structure on the classification results, as well as the advantages and limitations of different network structures, are discussed. Results suggest that BNs are able to satisfactorily deal with incomplete data, hence becoming a useful tool to predict the rock burst hazard at the initial stages of underground work.