Rock burst laws in deep mines based on combined model of membership function and dominance-based rough set

Abstract

Rock bursts are spontaneous, violent fracture of rock that can occur in deep mines, and the likelihood of rock bursts occurring increases as depth of the mine increases. Rock bursts are also affected by the compressive strength, tensile strength, tangential strength, elastic energy index, etc. of rock, and the relationship between these factors and rock bursts in deep mines is difficult to analyze from quantitative point. Typical rock burst instances as a sample set were collected, and membership function was introduced to process the discrete values of these factors with the discrete factors as condition attributes and rock burst situations as decision attributes. Dominance-based rough set theory was used to generate preference rules of rock burst, and eventually rock burst laws analysis in deep mines with preference relation was taken. The results show that this model for rock burst laws analysis in deep mines is more reasonable and feasible, and the prediction results are more scientific.

Keywords

deep mine / rock burst / membership function / dominance relation / rough set

References

[1] WangY-c, ShangY-q, SunH-y, YanX-s. Study of prediction of rockburst intensity based on efficacy coefficient method [J]. Rock and Soil Mechanics, 2010, 31(2): 529-534

[2] DongL-j, LiX-b, PengK. Prediction of rockburst classification using random forest [J]. Transactions of Nonferrous Metals Society of China, 2013, 23: 472-477

[3] PanQ-x, CaiM. Dynamic analysis for covert channel based on grey relational analysis algorithm [J]. The Journal of China Universities of Posts and Telecommunications, 2012, 19(1): 108-111

[4] MihirK, ChakrabortY. Membership function based rough set [J]. International Journal of Approximate Reasoning, 2014, 23: 402-411

[5] BaiS-m, ChenS. Evaluating students’ learning achievement using fuzzy membership functions and fuzzy rues [J]. Expert Systems with Applications, 2008, 34: 399-410

[6] FanT-f, LiauC-j, LiuD-r. Dominancebased fuzzy rough set analysis of uncertain and possibility data tables [J]. International Journal on Approximate Reasoning, 2011, 52: 1283-1297

[7] QianY-h L S-y, LiangJ-y. Pessimistic rough set based decisions: A multigranulation fusion strategy [J]. Information Sciences, 2014, 264: 196-210

[8] HuangB, WeiD-k, LiH-x, ZhuangY-l. Using a rough set model to extract rules in dominance-based intervalvalued intuitionistic fuzzy information systems [J]. Information Sciences, 2013, 221: 215-229

[9] YahyaouiH, AlmullaM, HalaS O. A novel non-functional matchmaking approach between fuzzy user queries and real world web services based on rough sets [J]. Future Generation Computer Systems, 2014, 35: 27-38

[10] KaramiJ, AlimohammadiA, SeifouriT. Water quality analysis using a variable consistency dominance-based rough set approach [J]. Computers, Environment and Urban Systems, 2014, 43: 25-33

[11] WangL, LuZ-l, GaoQ. A numerical study of rock burst development and strain energy release [J]. International Journal of Mining Science and Technology, 2012, 22: 675-680

[12] HeH, DouL-m, FanJ, DuT-t, SunX-l. Deep-hole directional fracturing of thick hard roof for rockburst prevention [J]. Tunnelling and Underground Space Technology, 2012, 32: 34-43

[13] WangY-c, ShangY-q, SunH-y, YanX-s. Study of prediction of rock burst intensity based on efficacy coefficient method [J]. Rock and Soil Mechanics, 2010, 31(2): 529-534

[14] JiangD-w, YangT. Prediction of rock burst intensity based on equivalent numerical method [J]. Journal of Water Resources & Water Engineering, 2014, 25(1): 210-213

[15] WenB, LiH-g. An approach to formulation of FNLP with complex piecewise linear membership functions [J]. Chinese Journal of Chemical Engineering, 2014, 22(4): 411-417

[16] DempeS, RuziyevaA. On the calculation of a membership function for the solution of a fuzzy linear optimization problem [J]. Fuzzy Sets and Systems, 2012, 188: 58-67