Study on stability classification of underground engineering surrounding rock based on concept lattice—TOPSIS

In order to classify surrounding rock stability of underground engineering efficiently and correctly, a model for classification of surrounding rock stability of underground engineering based on concept lattice and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was proposed. The surrounding rock of Jinzhou LPG cavern in China was selected as an engineering case for modeling. Considering the general factors affecting the stability of the underground surrounding rock and combined with the actual situation of the project, five indexes, namely, rock quality designation ( RQD ), saturated uniaxial compressive strength ( R c ), integrality coefficient ( K v ), longitudinal wave velocity ( V pm ), and fractal dimension ( D ), were selected as the indexes of the stability classification of surrounding rock. Firstly, five indexes were reduced by using the concept lattice theory. By analyzing the two groups of reduction, RQD , R c , and V pm were selected as the final classification indexes. Then, the stability of surrounding rock was classified by TOPSIS, and the classification results were consistent with the measured results. Finally, the established model was applied to the stability classification of several other surrounding rocks in Jinzhou LPG cavern. The results show that the classification results using TOPSIS based on concept lattice agreed well with those of fuzzy recognition direct method, fuzzy recognition indirect method, multistage fuzzy clustering method, and rock mass basic quality index (BQ) which based on five classification indexes. The constructed model reduces indexes required by classification of surrounding rock stability and improves efficiency.

[1]  G. R. Khanlari,et al.  Assessment of a Modified Rock Mass Classification System for Rock Slope Stability Analysis in the Q-system , 2015 .

[2]  Jan Konecny,et al.  Note on representing attribute reduction and concepts in concept lattice using graphs , 2017, Soft Comput..

[3]  Yong Liu,et al.  A knowledge acquisition method based on concept lattice and inclusion degree for ordered information systems , 2019, International Journal of Machine Learning and Cybernetics.

[4]  Hossein Jalalifar,et al.  Prediction of rock mass rating system based on continuous functions using Chaos–ANFIS model , 2015 .

[5]  Bo Meng,et al.  A Novel Model of the Ideal Point Method Coupled with Objective and Subjective Weighting Method for Evaluation of Surrounding Rock Stability , 2016 .

[6]  Cengiz Kahraman,et al.  A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets , 2019, Eng. Appl. Artif. Intell..

[7]  Jianchun Li,et al.  Quantitative assessments of the correlations between rock mass rating (RMR) and geological strength index (GSI) , 2019, Tunnelling and Underground Space Technology.

[8]  W. Feng,et al.  Quantitative Evaluation and Classification Method of the Cataclastic Texture Rock Mass Based on the Structural Plane Network Simulation , 2018, Rock Mechanics and Rock Engineering.

[9]  Bo Li,et al.  Attribute reduction and rule acquisition of formal decision context based on object (property) oriented concept lattices , 2019, Int. J. Mach. Learn. Cybern..

[10]  Jinhai Li,et al.  Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy , 2017, Int. J. Mach. Learn. Cybern..

[11]  Qi Yu-liang Method of rough sets-back propagation neural network and its application to identification of surrounding rock stability , 2011 .

[12]  Heriberto Pérez-Acebo,et al.  Correlation between Bieniawski’s RMR index and Barton’s Q index in fine-grained sedimentary rock formations , 2017 .

[13]  R. Su,et al.  A new rock mass classification system QHLW for high-level radioactive waste disposal , 2015 .

[14]  Jan Konecny,et al.  On attribute reduction in concept lattices: The polynomial time discernibility matrix-based method becomes the CR-method , 2019, Inf. Sci..

[15]  J. D. Fernández-Gutiérrez,et al.  Correlación entre el índice RMR de Bieniawski y el índice Q de Barton en formaciones sedimentarias de grano fino , 2017 .

[16]  Shitan Gu,et al.  Numerical Investigation on Factors Affecting Stability of Roadway Surrounding Rock with Fractured Roof , 2018, Geotechnical and Geological Engineering.

[17]  M. Hajiazizi,et al.  Seismic analysis of the rock mass classification in the Q-system , 2013 .

[18]  Hujun He,et al.  Study and Application on Stability Classification of Tunnel Surrounding Rock Based on Uncertainty Measure Theory , 2014 .

[19]  Dongmei Huang,et al.  Key Factors Identification and Risk Assessment for the Stability of Deep Surrounding Rock in Coal Roadway , 2019, International journal of environmental research and public health.

[20]  Shivani Guru,et al.  A comparative study on performance measurement of Indian public sector banks using AHP-TOPSIS and AHP-grey relational analysis , 2019, OPSEARCH.

[21]  Jason Papathanasiou,et al.  A decision support system for multiple criteria alternative ranking using TOPSIS and VIKOR in fuzzy and nonfuzzy environments , 2019, Fuzzy Sets Syst..

[22]  V. Santos,et al.  Estimating RMR Values for Underground Excavations in a Rock Mass , 2018 .

[23]  Jin Juliang Risk evaluation of surrounding rock stability based on stochastic simulation of multi-element connection number and triangular fuzzy numbers , 2011 .

[24]  Jun Yu Li,et al.  A Novel Model of Set Pair Analysis Coupled with Extenics for Evaluation of Surrounding Rock Stability , 2015 .

[25]  Jing Wang,et al.  Advance optimized classification and application of surrounding rock based on fuzzy analytic hierarchy process and Tunnel Seismic Prediction , 2014 .

[26]  Yang Liu,et al.  Stability Prediction Model of Roadway Surrounding Rock Based on Concept Lattice Reduction and a Symmetric Alpha Stable Distribution Probability Neural Network , 2018 .

[27]  Bulent Tiryaki,et al.  Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees , 2008 .