AUTHOR=Wang Jiachuang , Huang Mingjian , Guo Jiang 

TITLE=Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

JOURNAL=Frontiers in Physics

VOLUME=8

YEAR=2021

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2020.593701

DOI=10.3389/fphy.2020.593701

ISSN=2296-424X

ABSTRACT=<p>Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength <italic>σ</italic><sub><italic>c</italic></sub> (<italic>I</italic><sub>1</sub>), ratio of the uniaxial compressive strength to the tensile strength <italic>σ</italic><sub><italic>c</italic></sub><italic>/σ</italic><sub><italic>t</italic></sub> (brittleness coefficient, <italic>I</italic><sub>2</sub>), elastic deformation energy index <italic>W</italic><sub><italic>et</italic></sub> (<italic>I</italic><sub>3</sub>), ratio of the maximum tangential stress to the uniaxial compressive strength <italic>σ</italic><sub><italic>θ</italic></sub> /<italic>σ</italic><sub><italic>c</italic></sub> (stress coefficient, <italic>I</italic><sub>4</sub>) of the rock, depth of the roadway <italic>H</italic> (<italic>I</italic><sub>5</sub>), and integrity coefficient of the rock mass <italic>K</italic><sub><italic>v</italic></sub> (<italic>I</italic><sub>6</sub>) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.</p>