Abstract
Rock burst has always been a major problem in deep underground engineering with high stress, and rock burst strength evaluation has become an important research topic. To effectively predict the rock burst hazard in underground rock mass engineering, a cloud model (CM) rock burst intensity evaluation method based on the CRITIC method and order relation analysis method (G1) was proposed in this paper. First, a rock’s uniaxial compressive strength σc, tangential stress σθ, uniaxial tensile strength σt, ratio of uniaxial compressive strength to tensile strength σc/σt (brittleness coefficient), ratio of tangential stress to uniaxial compressive strength σθ/σc (stress coefficient), elastic deformation energy index Wet, and depth of cover H were selected as evaluation indices of rock burst intensity. Ninety-five groups of rock burst measured data at home and abroad were selected, and the objective weight and subjective weight of each index were calculated by using the CRITIC method and G1 method, respectively. The comprehensive weight was determined according to the combined weighting method of game theory, and the sensitivity of each evaluation index was analyzed. By utilizing a forward cloud generator, the membership degrees of different rock burst grades were calculated, and then the rock burst intensity grades of the samples were evaluated and compared with the evaluation results of the CRITIC-CM method and G1-CM method and the actual grades. Finally, the rock burst classification ability of the model was analyzed. To better verify the accuracy and reliability of this model, the rock burst case of the W39 line in the Chengchao Iron Mine was analyzed by using this model. The research results show that the rock burst evaluation results based on CRITIC-G1-CM are basically consistent with the actual rock burst grade, and the rock burst intensity grade evaluation model has good practicability and reliability.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Farhadian H (2021) A new empirical chart for rockburst analysis in tunnelling: Tunnel rockburst classification (TRC). Int J Min Sci Technol 31(4):603–610. https://doi.org/10.1016/j.ijmst.2021.03.010
Blake W, Hedley DGF (2003). Rockbursts: case studies from North American hard-rock mines. New York: Society for Mining, Metallurgy, and Exploration:121
Liu Z, Shao J, Xu W, Meng Y (2013) Prediction of rock burst classification using the technique of cloud models with attribution weight. Nat Hazards 68(2):549–568. https://doi.org/10.1007/s11069-013-0635-9
Adoko AC, Gokceoglu C, Wu L, Zuo QJ (2013) Knowledge-based and data-driven fuzzy modeling for rockburst prediction. Int J Rock Mech Min Sci 61:86–95. https://doi.org/10.1016/j.ijrmms.2013.02.010
Li DY, Liu CY, Gan WY (2009) A new cognitive model: cloud model. Int J Intell Syst 24(3):357–375. https://doi.org/10.1002/int.20340
Russenes BF (1974) Analysis of rock spalling for tunnels in steep valleysides. Trondheim: Norwegian Institute of Technology
Barton N, Lien R, Lunde J (1975) Engineering classification of rock masses for the design of tunnel support. Int J Rock Mech Mining Sci Geomech Abstracts 12(5–6):77. https://doi.org/10.1016/0148-9062(75)91319-4
Turchaninov IA, Markov GA, Lovchikov AV (1981) Conditions of changing of extra-hard rock into weak rock under the influence of tectonic stresses of massifs. Tokyo: Proceedings of International Symposium Weak Rock:555–559.
Hoek E, Brown ET (1980) Underground excavation in rock. Institute of Mining and Metallurgy, London
Kidybinski AQ (1981) Bursting liability indices of coal. Int J Rock Mech Min Sci Geomech Abstr 18(4):295–304
Chen W, Lv S, Guo X, Qiao C (2009) Study on confining pressure relief test and rock burst criterion based on energy principle. Chin J Rock Mech Eng 08:1530–1540
Lu J (1986) Study on rock burst mechanism of hydraulic diversion tunnel. Proceedings of the First National Symposium on Numerical Calculation and Model Test of Rock Mechanics, Chengdu: Southwest Jiaotong University Press:210–214.
Xu M, Du Z, Yao G, Liu Z (2008) Prediction of rock burst in deep mining of Chengchao Iron Mine. Chin J Rock Mech Eng S1:2921–2928
Zhou X, Zhang G, Song Y, Hu S, Liu M, Li J (2019) Evaluation of rock burst intensity based on annular grey target decision-making model with variable weight. Arab J Geosci 12(2):43. https://doi.org/10.1007/s12517-018-4193-z
Li N, Feng X, Jimenez R (2017) Predicting rock burst hazard with incomplete data using Bayesian networks. Tunn Undergr Space Technol 61:61–70. https://doi.org/10.1016/j.tust.2016.09.010
Xue Y, Li Z, Li S, Qiu D, Tao Y, Wang L, Yang WM, Zhang K (2017) Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation. Bull Eng Geol Env. https://doi.org/10.1007/s10064-017-1117-1
Shukla R, Khandelwal M, Kankar PK (2021) Prediction and assessment of rock burst using various meta-heuristic approaches. Mining, Metall Explor 38(3):1375–1381. https://doi.org/10.1007/s42461-021-00415-w
Li Z, Xue Y, Li S, Qiu D, Zhang L, Zhao Y, Zhou B (2020) Rock burst risk assessment in deep-buried underground caverns: a novel analysis method. Arab J Geosci 13(11):388. https://doi.org/10.1007/s12517-020-05328-4
Lin Y, Zhou K, Li J (2018) Application of cloud model in rock burst prediction and performance comparison with three machine learning algorithms. IEEE Access 6:30958–30968. https://doi.org/10.1109/access.2018.2839754
Zhou K, Lin Y, Deng H, Li J, Liu C (2016) Prediction of rock burst classification using cloud model with entropy weight. Trans Nonferrous Metals Soc China 26(7):1995–2002. https://doi.org/10.1016/s1003-6326(16)64313-3
Wang J, Liu P, Ma L, He M (2021) A rockburst proneness evaluation method based on multidimensional cloud model improved by control variable method and rockburst database. Lithosphere 4:5354402. https://doi.org/10.2113/2022/5354402
Li D, Meng H, Shi X (1995) Membership clouds and membership cloud generators. J Comput Res Dev 06:15–20
Zhu M, Hahn A, Wen YQ (2018) Identification-based controller design using cloud model for course-kee** of ships in waves. Eng Appl Artif Intell 75:22–35. https://doi.org/10.1016/j.engappai.2018.07.011
Zang W, Ren L, Zhang W, Liu X (2018) A cloud model based DNA genetic algorithm for numerical optimization problems. Futur Gener Comput Syst 81:465–477. https://doi.org/10.1016/j.future.2017.07.036
Gao H, **e G, Liu H, Zhang X, Li D (2017) Lateral control of autonomous vehicles based on learning driver behavior via cloud model. J China Univ Posts Telecommun 24(2):10–17. https://doi.org/10.1016/s1005-8885(17)60194-8
Khedim F, Labraoui N, Ari AAA (2018) A cognitive chronometry strategy associated with a revised cloud model to deal with the dishonest recommendations attacks in wireless sensor networks. J Netw Comput Appl. https://doi.org/10.1016/j.jnca.2018.09.001
Li J, Wang MW, Xu P, Xu PC (2014) Classification of stability of surrounding rock using cloud model. Chin J Geotech Eng 36(1):83–87
Gong Y (2012) Comprehensive assessment on ecological risk of Hexi corridor urbanization based on normal cloud model and entropy weight. J Arid Land Resour Environ 26(5):169–174
Peng T, Deng H (2020) Comprehensive evaluation on water resource carrying capacity in karst areas using cloud model with combination weighting method: a case study of Guiyang, southwest China. Environ Sci Pollut Res Int 27:37057–37073
Yao J, Wang G, Xue B, Wang P, Hao F, **e G, Peng Y (2019) Assessment of lake eutrophication using a novel multidimensional similarity cloud model. J Environ Manag 248:109259. https://doi.org/10.1016/j.jenvman.2019.109259
Peng T, Deng H, Lin Y, ** Z (2021) Assessment on water resources carrying capacity in karst areas by using an innovative DPESBRM concept model and cloud model. Sci Total Environ 767:144353. https://doi.org/10.1016/j.scitotenv.2020.144353
Tian YG, Du YH, Qin DH, Liao XL (2011) Flood risk evaluation methods based on data field and cloud model. China Safety Science 21(8):158–163. https://doi.org/10.16265/j.cnki.issn1003-3033.2011.08.007
Afraei S, Shahriar K, Madani SH (2017) Statistical analysis of rock-burst events in underground mines and excavations to present reasonable data-driven predictors. J Stat Comput Simul 87(17):3336–3376. https://doi.org/10.1080/00949655.2017.1367000
Mutke G, Dubiński J, Lurka A (2015) New criteria to assess seismic and rock burst hazard in coal mines / Nowe Kryteria Dla Oceny Zagrożenia Sejsmicznego I Tąpaniami W Kopalniach Węgla Kamiennego. Arch Min Sci 60(3):743–760. https://doi.org/10.1515/amsc-2015-0049
Wang Y, Li Q (1998) Comprehensive evaluation method of fuzzy mathematics for rock burst prediction. J Rock Mech Eng 17(5):495–501
Wang J, Huang M, Guo J (2021) Rock burst evaluation using the critic algorithm-based cloud model. Front Phys 8:593701. https://doi.org/10.3389/fphy.2020.593701
Wang Q (2008) Aggregate analysis of group decision making based on G1 method. Management Innovation & Industrial Engineering for the Rise of Central China
Yi L, Zhao J, Yu W, Long G, Sun H, Li W (2020) Health status evaluation of catenary based on normal fuzzy matter-element and game theory. J Electr Eng Technol. https://doi.org/10.1007/s42835-020-00481-y
Wang MW, Xu P, Xu PC (2014) Classification of stability of surrounding rock using cloud model. Chin J Geotech Eng 36(1):83–87. https://doi.org/10.11779/CJGE201401006
Tian R (2020) Research and application of prediction model of rockburst intensity level based on machine learning.(Doctoral dissertation, Inner Mongolia University of Science and Technology). https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2021&filename=1021543708.nh
Zhang CQ, Zhou H, Feng XT (2011) An index for estimating the stability of brittle surrounding rock mass: FAI and its engineering application. Rock Mech Rock Eng 44(4):401–414. https://doi.org/10.1007/s00603-011-0150-9
Zhou J, Li X, Shi X (2012) Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Saf Sci 50(4):629–644. https://doi.org/10.1016/j.ssci.2011.08.065
Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 52204156) and the Sichuan Natural Science Foundation (Grant No. 2022NSFSC147).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing Interests
The authors declare that they have no conflicts of interest. No conflict of interest exists in the submission of this manuscript, and the manuscript has been approved by all authors for publication. The authors declare that except for the preprint published in the research square, it has been published elsewhere. All the authors listed have approved the manuscript that is enclosed.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, Q., Liu, C., Guo, S. et al. Evaluation of the Rock Burst Intensity of a Cloud Model Based on the CRITIC Method and the Order Relation Analysis Method. Mining, Metallurgy & Exploration 40, 1849–1863 (2023). https://doi.org/10.1007/s42461-023-00838-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42461-023-00838-7