Abstract
Rockburst is a highly destructive geological hazard that can cause casualties and equipment damage. To achieve high-accuracy discrimination of rockburst intensity, this article proposes an improved model that addresses the inefficient maximum membership principle used in traditional rockburst fuzzy evaluation models. The stress coefficient σθ/σc, brittleness coefficient σc/σt, and elastic deformation energy index Wet are selected as evaluation indicators for rockburst classification. Subjective and objective weights are obtained using the Delphi method and entropy weight method (EWM). Three types of membership function distribution forms are then used to obtain the membership degrees of each indicator to rockburst grades: trapezoidal membership function (TMF), normal membership function (NMF), and quadratic parabolic membership function (QPMF). Finally, six traditional models and six improved models are established using the maximum membership principle (MMP) and weighted average-maximum membership principle combination evaluation principle (WMP), respectively. Based on the analysis of 100 sets of rockburst field data, the accuracy, precision, recall, and F1-score of the improved evaluation model are increased by 11.3%, 0.097, 0.068, and 0.089, respectively, compared to the traditional model. The Delphi-NMF-WMP model is selected as the best model, with four performance indices reaching 97.0%, 0.979, 0.979, and 0.978. The best model is applied to evaluate the rockburst intensity of the Cangling Tunnel, Dongguashan Copper Mine, and Jiangbian Hydropower Station Diversion Tunnel, with evaluation results consistent with the actual situation, demonstrating the reliability and scientificity of the model.
Similar content being viewed by others
Data Availability
The authors confirm that the data supporting the findings of this study are available within the article [and/or its supplementary materials].
Abbreviations
- σ θ :
-
The maximum tangential stress of the surrounding rock
- σ c :
-
The uniaxial compressive strength of rock
- σ t :
-
The tensile strength of rock
- σ θ /σ c :
-
The stress ratio
- σ c /σ t :
-
The rock brittleness ratio
- W et :
-
The elastic energy index
References
Li ML, Li KG, Qin QC, Yue R, Xu G (2023) Rockburst estimation model based on IEWM-SCM and its application. Environ Earth Sci 82(3):88. https://doi.org/10.1007/s12665-023-10764-y
Kadkhodaei MH, Ghasemi E (2022) Development of a semi-quantitative framework to assess rockburst risk using risk matrix and logistic model tree. Geotech Geol Eng 40(3):3669–3685. https://doi.org/10.1007/s10706-022-02122-9
Kadkhodaei MH, Ghasemi E, Sari M (2022) Stochastic assessment of rockburst potential in underground spaces using Monte Carlo simulation. Environ Earth Sci 81(18):447. https://doi.org/10.1007/s12665-022-10561-z
Blake W, Hedley DGF (2003) Rockbursts: case studies from North American hard-rock mines. Society for Mining, Metallurgy, and Exploration, Littleton, Colo
Tang BY (2000) Rockburst control using destress blasting. Dissertation, McGill University
Kaiser PK, Cai M (2012) Design of rock support system under rockburst condition. J Rock Mech Geotech Eng 4(3):215–227. https://doi.org/10.3724/SP.J.1235.2012.00215
Yu Q, Tang CA, Li LC, Cheng GW, Tang LX (2015) Study on rockburst nucleation process of deep-buried tunnels based on microseismic monitoring. Shock Vib 685437. https://doi.org/10.1155/2015/685437
Naji AM, Emad MZ, Rehman H, Yoo H (2019) Geological and geomechanical heterogeneity in deep hydropower tunnels: a rock burst failure case study. Tunn Undergr Space Technol 84:507–521. https://doi.org/10.1016/j.tust.2018.11.009
He SQ, Song DZ, Mitri H, He XQ, Chen JQ, Li ZL, Xue YR, Chen T (2021) Integrated rockburst early warning model based on fuzzy comprehensive evaluation method. Int J Rock Mech Min Sci 142(4). https://doi.org/10.1016/j.ijrmms.2021.104767
Shin JH, Moon HG, Chae SE (2011) Effect of blast-induced vibration on existing tunnels in soft rocks. Tunn Undergr Space Technol 26(1):51–61. https://doi.org/10.1016/j.tust.2010.05.004
Malkowski P, Niedbalski Z (2020) A comprehensive geomechanical method for the assessment of rockburst hazards in underground mining. Int J Min Sci Technol 30(3):345–355. https://doi.org/10.1016/j.ijmst.2020.04.009
Pu YY, Apel D, Xu HW (2018) A principal component analysis/fuzzy comprehensive evaluation for rockburst potential in kimberlite. Pure Appl Geophys 175(6):2141–2151. https://doi.org/10.1007/s00024-018-1790-4
Li YF, Wang C, Xu JK, Zhou ZH, Xu JH, Cheng JW (2021) Rockburst prediction based on the KPCA-APSO-SVM model and its engineering application. Shock Vib S1:1–12. https://doi.org/10.1155/2021/7968730
Russenes BF (1974) Analysis of rock spalling for tunnels in steep valley sides. Dissertation, Norwegian Institute of Technology
Hoek E, Brown ET (1997) Practical estimates of rock mass strength. Int J Rock Mech Min Sci 34(8):1165–1186. https://doi.org/10.1016/S1365-1609(97)80069-X
Kidybinski A (1981) Bursting liability indices of coal. Int J Rock Mech Min Sci Geomech Abstr 18(4):295–304. https://doi.org/10.1016/0148-9062(81)91194-3
Gao FQ, Yuan GY, Liu WJ, Peng XY (2023) Experimental study of strainbursts caused by stress concentration and local mine stiffness decrease as mining proceeds. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-023-03445-6
Zhou J, Li XB, Shi XZ (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
Zhou J, Li X, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Sp Technol 81:632–659. https://doi.org/10.1016/j.tust.2018.08.029
Li ML, Li KG, Qin QC, Yue R, Shi J (2023) Research and application of an intelligent prediction of rock bursts based on a Bayes-optimized convolutional neural network. Int J Geomech 23(5):04023042. https://doi.org/10.1061/IJGNAI.GMENG-8213
Liang WZ, Zhao GY (2022) A review of long-term and short-term rockburst risk evaluations in deep hard rock. Chin J Rock Mech Eng 41(01):19–39. https://doi.org/10.13722/j.cnki.jrme.2021.0165
Luo H, Fang Y, Wang JF, Wang YB, Liao H, Yu T, Yao ZG (2023) Combined prediction of rockburst based on multiple factors and stacking ensemble algorithm. Undergr Space 12:241–261. https://doi.org/10.1016/j.undsp.2023.05.003
Hu XM, Huang LQ, Chen JZ, Li XB, Zhang HZ (2023) Rockburst prediction based on optimization of unascertained measure theory with normal cloud. Complex Intell Syst. https://doi.org/10.1007/s40747-023-01127-y
Owusu-Ansah D, Tinoco J, Lohrasb F, Martins F, Matos J (2023) A decision tree for rockburst conditions prediction. Appl Sci-Basel 13(11):6655. https://doi.org/10.3390/app13116655
Agrawal H, Durucan S, Cao WZ, Korre A, Shi JQ (2022) Rockburst and gas outburst forecasting using a probabilistic risk assessment framework in longwall top coal caving faces. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-022-03076-3
Zhang QJ , Liu CJ, Guo S, Wang WT, Luo HM, Jiang YH (2023) Evaluation of the rock burst intensity of a cloud model based on the CRITIC method and the order relation analysis method. Mining Metall Explor. https://doi.org/10.1007/s42461-023-00838-7
Sari M (2019) Incorporation of uncertainty in estimating the rock mass uniaxial strength using a fuzzy inference system. Arab J Geosci 12(2). https://doi.org/10.1007/s12517-018-4169-z
Tolga AC, Basar M (2022) The assessment of a smart system in hydroponic vertical farming via fuzzy MCDM methods. J Intell Fuzzy Syst 42(1):1–12. https://doi.org/10.3233/JIFS-219170
Liang WZ, Zhao GY, Wu H, Dai B (2019) Risk assessment of rockburst via an extended MABAC method under fuzzy environment. Tunn Undergr Space Technol 83:533–544. https://doi.org/10.1016/j.tust.2018.09.037
Cai W, Dou LM, Zhang M, Cao WZ, Shi JQ, Feng LF (2018) A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring. Tunn Undergr Space Technol 80:232–245. https://doi.org/10.1016/j.tust.2018.06.029
Rastegarmanesh A, Moosavi M, Kalhor A (2020) A data-driven fuzzy model for prediction of rockburst. Georisk 15(2):152–164. https://doi.org/10.1080/17499518.2020.1751208
Wang AF, Yang XT, Gu XB (2023) The risk assessment of rockburst intensity in the highway tunnel based on the variable fuzzy sets theory. Sci Rep 13(1). https://doi.org/10.1038/s41598-022-27058-1
Kang QR, Xia YD, Shi MH, Zhang WZ, Wang WQ, Kong DH, Wang YP (2022) Evaluation of rock burst propensity and rock burst mechanism in deep phosphate mines: a case study of Sujiapo Phosphate Mine, Hubei Province, China. Adv Mater Sci Eng 7874016. https://doi.org/10.1155/2022/7874016
Li ZY, Zhong ZL, Cao XX, Hou BW, Li LY (2023) Robustness analysis of shield tunnels in non-uniformly settled strata based on fuzzy set theory. Comput Geotech 162:105670. https://doi.org/10.1016/j.compgeo.2023.105670
Chen W, Sun HQ, Wang H, Wu QB, Ma C, Cha ZY (2022) Entropy weight-set pair analysis model of collapse risk assessment in mountain tunnels and its engineering application. Adv Eng Sci. https://doi.org/10.15961/j.jsuese.202200247.
Yu Y, Qiu D, Yan RT (2022) A multi-modal and multi-scale emotion-enhanced inference model based on fuzzy recognition. Complex Intell Syst 8(2):1071–1084. https://doi.org/10.1007/s40747-021-00579-4
Wang AB (2021) Research on evaluation methods of aerospace software quality. Dissertation, University of Chinese Academy of Sciences
Zhou YL, Zhou W, Lu X, Jiskani IM, Cai QX, Liu P, Li L (2020) Evaluation index system of green surface mining in China. Mining Metall Explor 37(4):1093–1103. https://doi.org/10.1007/s42461-020-00236-3
Madanda VC, Sengani F, Mulenga F (2023) Applications of fuzzy theory-based approaches in tunnelling geomechanics: a state-of-the-art review. Mining Metall Explor 40(3):819–837. https://doi.org/10.1007/s42461-023-00767-5
Sun N, Li CL, Guo BY, Sun XK, Yao YK, Wang Y (2023) Urban flooding risk assessment based on FAHP-EWM combination weighting: a case study of Beijing. Geomat Nat Hazards Risk 14(1):2240943. https://doi.org/10.1080/19475705.2023.2240943
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(4):86–95. https://doi.org/10.1016/j.ijrmms.2013.02.010
Yang HT, Jia C, Li X, Yang F, Wang C, Yang X (2022) Evaluation of seawater intrusion and water quality prediction in Dagu River of North China based onfuzzy analytic hierarchy process exponential smoothing method. Environ Sci Pollut Res 29(44):66160–66176. https://doi.org/10.1007/s11356-022-19871-y
Yue SH, Rong XL, Ma HT, Lu J (2021) Electrical impedance tomography algorithm based on fuzzy operator. Tianjin Univ 54(2):179–185. https://doi.org/10.11784/tdxbz202002046
Luo DZ, Li HT, Wu Y, Li D, Yang XG, Yao Q (2021) Cloud model-based evaluation of landslide dam development feasibility. PLoS One 16(5):e0251212. https://doi.org/10.1371/journal.pone.0251212
Guo DY (2022) Research on evaluation index and calculation method of smart port construction effect. Dissertation, Dalian University of Technology
He MC, Miao JL, Li DJ, Wang CG (2007) Experimental study on rockburst processes of granite specimen at great depth. Chin J Rock Mech Eng 5:865–876
Li XB (2014) Rock dynamics: fundamentals and applications. Science Press, Beijing
Dietz M, Oremek GM, Groneberg DA, Bendels MHK (2018) Was ist ein Gebirgsschlag? Zbl Arbeitsmed 68:45–49. https://doi.org/10.1007/s40664-017-0215-z
Ortlepp WD (1997) Rock fracture and rockbursts: an illustrative study. South African Institute of Mining and Metallurgy, Johannesburg
Faradonbeh RS, Taheri A, Sousa LRE, Karakus M (2018) Rockburst assessment in deep geotechnical conditions using true-triaxial tests and data-driven approaches. Int J Rock Mech Min Sci 128:104279. https://doi.org/10.1016/j.ijrmms.2020.104279
Zhai SB (2022) True triaxial experimental study of rockburst and related failure in deep underground tunnel. Dissertation, Guangxi University
Chen L (2023) Study on evolution mechanism of rock fracture and rockburst prediction in deep high stress environment. Dissertation, University of Science and Technology Beijing
Hoek E, Brown ET (1980) Underground excavations in rock. CRC Press, London
Li DY (2010) Study on the spalling failure of hard rock and the mechanism of strainburst under high in-situ stresses. Dissertation, Central South University
Feng XT, Yang CX, Kong R, Zhao J, Zhou YY, Yao ZB, Hu L (2021) Excavation-induced deep hard rock fracturing: methodology and applications. J Rock Mech Geotech Eng 14(1):1–34. https://doi.org/10.1016/j.jrmge.2021.12.003
Feng XT, Xiao YX, Feng GL, Yao ZB, Chen BR, Yang CX, Sun GS (2019) Study on the development process of rockbursts. Chin J Rock Mech Eng 38(04):649–673. https://doi.org/10.13722/j.cnki.jrme.2019.0103
Feng XT, Chen BR, Zhang CQ, Li SJ, Wu SY (2013) Mechanism, warning and dynamic control of rockburst development processes. Science Press
Peng Z, Wang YH, Li TJ (1996) Griffith theory and criterion for judging rock bursts. Chin J Rock Mech Eng S1:491–495
Chen PY, Yu HM, Shi HP (2014) Prediction model for rockburst based on weighted back analysis and standardized fuzzy comprehensive evaluation. Rock Mech Eng 33(10):2154–2160. https://doi.org/10.13722/j.cnki.jrme.2014.10.024
Li ML, Li KG, Qin QC (2023) A rockburst prediction model based on extreme learning machine with improved Harris Hawks optimization and its application. Tunn Undergr Space Technol 134:104978. https://doi.org/10.1016/j.tust.2022.104978
Zhou J, Li XB, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civil Eng 30(5):4016003. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000553
Wang YH, Li WD, Li QG, Xu Y, Tan GH (1998) Fuzzy mathematics comprehensive evaluation method for rockburst prediction. Rock Mech Eng 17(5):15–23
Zhang LW, Zhang DY, Qiu DH (2010) Application of extension evaluation method in rockburst prediction based on rough set theory. J China Coal Soc. 35(9):1461–1465. https://doi.org/10.13225/j.cnki.jccs.2010.09.031
Yang JL, Li XB, Zhou ZL, Lin Y (2010) A fuzzy assessment method of rock-burst prediction based on rough set theory. Metal Mine 6:26–29
Zhang LX, Li CH (2009) Study on tendency analysis of rockburst and comprehensive prediction of different types of surrounding rock. Rinton Press, Princeton
Feng XT, Wang LN (1994) Rockburst prediction based on neural networks. Trans Nonferrous Met Soc China 4(1):7–14
Afraei S, Shahriar K, Madani SH (2019) Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, section 2: designing classifiers. Tunn Undergr Space Technol 84:522–537. https://doi.org/10.1016/j.tust.2018.11.011
Xue YG, Li ZQ, Li SC, Qiu DH, Tao YF, Wang L, Yang WM, Zhang K (2019) Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation. Bull Eng Geol Environ 78(1):417–429. https://doi.org/10.1007/s10064-017-1117-1
Zhang JF (2010) Study on prediction by stages and control technology of rockburst hazard of Daxiangling highway tunnel. Dissertation, Southwest Jiaotong Univ
Chen SM, Wu AX, Wang YM, Xu MG (2016) Prediction of rockburst intensity based on decision tree model. J Wuhan Univ Sci Technol 39(03):195–199
Qin SW, Chen JP, Wang Q (2009) Research on rockburst prediction with extenics evaluation based on rough set. Rinton Press, Princeton
Xu MG, Du ZJ, Yao GH, Liu ZP (2008) Rockburst prediction of Chengchao iron mine during deep mining. Chin J Rock Mech Eng S1:2921–2928
Yi YL, Cao P, Pu CZ (2010) Multi-factorial comprehensive estimation for Jinchuan’s deep typical rockburst tendency. Sci Technol Rev (Beijing, China). 28(02):76–80
Liang ZY (2004) Study on the pridiction and prevention of rockburst in the diversion tunnel of Jinping II hydropower. Dissertation, Chengdu Univ Technol
Wang YC, Shang YQ, Sun HY, Yan XS (2010) Study of prediction of rockburst intensity based on efficacy coefficient method. Rock Soil Mech 31(2):529–534. https://doi.org/10.16285/j.rsm.2010.02.017
Yang T, Li GW (2000) Study on rockburst prediction method based on the prior knowledge. Rock Mech Eng 4:429–431
Wang Y, Xu Q, Chai HJ, Li L, Xia YC, Wang XD (2013) Rock burst prediction in deep shaft based on RBF-AR model. J Jilin Univ, Earth Sci Ed. https://doi.org/10.13278/j.cnki.jjuese.2013.06.019
Hu M, Chen JH, Lu YG (2011) Research on rock burst prediction based on BP Neural Network and GA. Min Res Dev R&D Min 31(05):90–94. https://doi.org/10.13827/j.cnki.kyyk.2011.05.005
Luo L, Cao P (2012) Model of weighted distance discriminant analysis and application for deep roadway. J Cent South Univ (Sci Technol) 43(10):3971–3975
Su GS, Zhang Y, Chen GQ (2010) Identify rockburst grades for Jinping hydropower station using Gaussian II process for binary classification. IEEE Press, NJ
Liu ZJ, Yuan QP, Li JL (2008) Application of fuzzy probability model to model to prediction of classification of rockburst intensity. Rock Mech Eng 27(S1):3095–3103
Bai MZ, Wang LJ, Xu ZY (2002) Study on a neutral network model and its application in predicting the risk of rock burst. China Saf Sci J 12(4):65–69
Li SL, Feng XT, Wang YJ, Yang NG (2001) Evaluation of the tendency of deep well hard rockburst. J. Northeast. Univ. Nat Sci 1:60–63
Fu YH, Dong LJ (2009) Bayes discriminant analysis model and its application to the prediction and classification of rockburst. J China U Min Techno 38(04):528–533
Gong FQ, Li XB (2007) A distance discriminant analysis method for prediction of possibility and classification of rockburst and its application. Rock Mech Eng 5:1012–1018
Zhou KP, Lei T, Hu JH (2013) RS-TOPSIS model of rockburst prediction in deep metal mines and its application. Rock Mech Eng 32(S2):3705–3711
Hao J (2015) Study on surrounding rock quality evaluation and the stability for tunnel construction period in high geostress areas. Dissertation, Xinjiang Agric Univ
Christopher M (2016) Coal bursts in the deep longwall mines of the United States. Int J Coal Sci Technol 3(1):1–9. https://doi.org/10.1007/s40789-016-0102-9
Borch-Johnsen L, Andres-Jensen L, Folke F, Espersen MM, Amstrup SL, Frederiksen MS, Gjaerde LK, Hjelvang BR, Kristoffersen MJ, Lundby-Christensen L, Schroder M, Spangenberg KB, Lund S, Cortes D (2023) Development of video tutorials to help parents manage children with acute illnesses using a modified Delphi method. Acta Paediatr 112(7):1574–1585. https://doi.org/10.1111/apa.16805
Wang YC, Jing HW, Zhang Q, Wei LY, Xu ZM (2015) A normal cloud model-based study of grading prediction of rockburst intensity in deep underground engineering. Rock Soil Mech 36(4):1189–1194. https://doi.org/10.16285/j.rsm.2015.04.037
Zhang H, Li WJ, Miao PP, Sun B, Kong FQ (2020) Risk grade assessment of sudden water pollution based on analytic hierarchy process and fuzzy comprehensive evaluation. Environ Sci Pollut Res 27(1):469–481. https://doi.org/10.1007/s11356-019-06517-9
Venkatramanan S, Chung SY, Rajesh R, Lee SY, Ramkumar T, Prasanna MV (2015) Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea. Environ Sci Pollut Res 22(15):11209–11223. https://doi.org/10.1007/s11356-015-4290-4
Qin GJ, Zhang P, Hou XQ, Wu S, Wang YH (2020) Risk assessment for oil leakage under the common threat of multiple natural hazards. Environ Sci Pollut Res 27(14):16507–16520. https://doi.org/10.1007/s11356-020-08184-7
Tabbussum R, Dar AQ (2021) Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction. Environ Sci Pollut Res 28(20):25265–25282. https://doi.org/10.1007/s11356-021-12410-1
Ding XD, Wu JM (2006) Fuzzy classification of rock mass Adv. Sci Technol Water Resour 26(3):18–20. https://doi.org/10.3880/j.issn.1006-7647.2006.03.005
Huang SJ, Ma Y (2016) An identification method for the most probable failure members of RC frame structure under earthquakes based on fuzzy reliability. China Civ Eng 49(S1):61–65. https://doi.org/10.15951/j.tmgcxb.2016.s1.011
Peng YW, Lan H, Wang SW, Pan JF, Qi QX (2010) Dynamic pre-evaluation system of bursting hazard based on geological conditions. China Coal Soc 35(12):1997–2001. https://doi.org/10.13225/j.cnki.jccs.2010.12.010
Wang GS, Liu X, Hong BN, Sheng K, Qian XX (2022) Assessment of rock drillability by the method of analytic hierarchy process combined with fuzzy comprehensive evaluation. Arab J Geosci 15:67. https://doi.org/10.1007/s12517-021-09270-x
Wang YD, Jia YL, Tian YH, Xiao J (2022) Deep reinforcement learning with the confusion-matrix-based dynamic reward function for customer credit scoring. Expert Syst Appl 200:117013. https://doi.org/10.1016/j.eswa.2022.117013
Zhou J, Yang PX, Peng PA, Khandelwal M, Qiu YG (2023) Performance evaluation of rockburst prediction based on PSO-SVM, HHO-SVM, and MFO-SVM hybrid models. Mining Metall Explor. https://doi.org/10.1007/s42461-022-00713-x
Guo J, Guo JW, Zhang QL, Huang MJ (2022) Research on rockburst classification prediction based on BP-SVM model. IEEE Access 10:50427–50447. https://doi.org/10.1109/ACCESS.2022.3173059
Chen C, Huang J, Liu L, Wu DR (2022) Data-driven Takagi-Sugeno fuzzy system modeling and predictive control of a pneumatic flexible joint. Control Theory Appl 39(4):633–642. https://doi.org/10.7641/CTA.2021.10156
Jia YP, Lu Q, Shang YQ (2013) Rockburst prediction using particle swarm optimization and general regression neural network. Rock Mech Eng 32(2):343–348. https://doi.org/10.3969/j.issn.1000-6915.2013.02.016
Wang C, Xu JH, Li YF, Wang TH, Wang QW (2023) Optimization of BP neural network model for rockburst prediction under multiple influence factors. Appl Sci-Basel 13(4):2741. https://doi.org/10.3390/app13042741
Qiu DH, Zhang LW, Li SC (2010) Weight back analysis method based on optimization theory. Geotech Eng 32(2):259–264
Wojtecki L, Iwaszenko S, Apel DB, Bukowska M, Makowka J (2022) Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings. J Rock Mech Geotech Eng 14(3):703–713. https://doi.org/10.1016/j.jrmge.2021.10.011
Kidega R, Ondiaka MN, Maina D, Jonah KAT, Kamran M (2022) Decision based uncertainty model to predict rockburst in underground engineering structures using gradient boosting algorithms. Geomech Eng 30(3):259–272. https://doi.org/10.12989/gae.2022.30.3.259
Funding
This research was supported by the Yunnan Fundamental Research Projects (202301AT070462), the National Natural Science Foundation of China (42367024), the Major Science and Technology Special Project of Yunnan Province (202202AG050014), the Yunnan Innovation Team (202105AE160023), and the College Students’ Innovation and Entrepreneurship Training Programs of Yunnan Province (S202210674092).
Author information
Authors and Affiliations
Contributions
Qiwei Wang: conceptualization, methodology, formal analysis, and writing—original draft; Chao Wang: conceptualization, methodology, formal analysis; Yu Liu and Jianhui Xu: writing—review and editing; Tuanhui Wang, Yuefeng Li, and Quanrui Liu: data curation and validation.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Conflict of Interest
The authors declare no competing interests.
Disclaimer
The funder did not play any role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; nor in the preparation, review, or approval of the manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1. Delphi method-fuzzy mathematical model evaluation results
No | Evaluating index | Actual grade | Evaluation results of traditional fuzzy models | Evaluation results of improved fuzzy models | ||||||
---|---|---|---|---|---|---|---|---|---|---|
σθ/σc | σc/σt | Wet | Trapezoid | Normal | Quadratic parabola | Trapezoid | Normal | Quadratic parabola | ||
1 | 0.11 | 31.23 | 7.40 | I | I | I | I | I | I | I |
2 | 0.20 | 36.04 | 2.29 | I | II* | I | II* | II* | I | II* |
3 | 0.19 | 47.93 | 1.87 | I | I | I | I | I | I | I |
4 | 0.13 | 6.67 | 1.39 | I | I | I | I | I | I | I |
5 | 0.19 | 6.67 | 1.39 | I | I | I | I | I | I | I |
6 | 0.23 | 6.67 | 1.39 | I | I | I | I | I | I | I |
7 | 0.28 | 9.68 | 1.92 | I | I | I | I | I | I | I |
8 | 0.11 | 27.22 | 7.00 | I | I | I | II* | I | I | I |
9 | 0.14 | 14.05 | 1.30 | I | I | I | I | I | I | II* |
10 | 0.22 | 36.42 | 1.75 | I | I | I | I | I | I | II* |
11 | 0.31 | 42.80 | 1.80 | I | I | I | I | I | I | I |
12 | 0.20 | 11.20 | 3.60 | I | I | I | III* | I | I | II* |
13 | 0.20 | 14.10 | 3.60 | I | I | I | III | I | I | II* |
14 | 0.28 | 42.73 | 2.17 | I | I | I | I | I | I | I |
15 | 0.11 | 29.40 | 2.04 | I | I | I | II* | I | I | II* |
16 | 0.23 | 7.52 | 1.50 | I | I | I | I | I | I | I |
17 | 0.43 | 45.90 | 1.70 | I | I | I | I | I | I | I |
18 | 0.22 | 36.42 | 1.75 | I | I | I | I | I | I | I |
19 | 0.11 | 31.2 | 7.40 | I | I | I | II* | I | I | I |
20 | 0.40 | 15.61 | 3.50 | II | II | II | II | II | II | II |
21 | 0.44 | 13.13 | 2.12 | II | II | II | II | II | II | II |
22 | 0.37 | 24.00 | 5.10 | II | II | II | III* | II | II | II |
23 | 0.45 | 11.2 | 2.03 | II | II | II | II | II | II | II |
24 | 0.67 | 26.80 | 0.85 | II | III* | III* | III* | II | II | III* |
25 | 0.56 | 20.40 | 2.00 | II | III* | III* | III* | II | II | III* |
26 | 0.46 | 20.40 | 2.00 | II | II | II | II | II | II | II |
27 | 0.49 | 19.70 | 2.30 | II | II | II | II | II | II | II |
28 | 0.28 | 23.60 | 4.90 | II | III* | III* | III* | II | II | II |
29 | 0.44 | 19.70 | 2.30 | II | II | II | II | II | II | II |
30 | 0.28 | 23.80 | 4.80 | II | III* | III* | III* | II | II | II |
31 | 0.46 | 19.70 | 2.30 | II | II | II | II | II | II | II |
32 | 0.42 | 19.70 | 2.30 | II | II | II | II | II | II | II |
33 | 0.56 | 34.30 | 1.90 | II | II | II | II | II | II | II |
34 | 0.30 | 20.40 | 5.00 | II | III* | III* | III* | III* | III* | II |
35 | 0.35 | 22.70 | 3.31 | II | II | II | II | II | II | II |
36 | 0.45 | 14.82 | 3.10 | II | II | II | II | II | II | II |
37 | 0.41 | 30.70 | 4.30 | II | II | II | II | II | II | II |
38 | 0.45 | 6.84 | 2.15 | II | II | II | II | II | II | II |
39 | 0.35 | 12.05 | 2.85 | II | II | II | II | II | II | II |
40 | 0.37 | 29.73 | 3.52 | II | II | II | II | II | II | II |
41 | 0.42 | 32.77 | 2.97 | II | II | II | II | II | II | II |
42 | 0.38 | 28.8 | 3.00 | II | II | II | II | II | II | II |
43 | 0.42 | 29.90 | 2.40 | II | II | II | II | II | II | II |
44 | 0.42 | 15.50 | 3.20 | II | II | II | II | II | II | II |
45 | 0.57 | 31.20 | 3.20 | II | II | II | II | II | II | II |
46 | 0.44 | 8.98 | 4.86 | II | II | II | II | II | II | II |
47 | 0.43 | 13.98 | 7.44 | III | IV* | IV* | IV* | III | III | III |
48 | 0.55 | 11.10 | 3.97 | III | II* | III | III | III | III | III |
49 | 0.34 | 23.97 | 6.60 | III | II* | II* | II* | III | III | III |
50 | 0.42 | 21.69 | 5.00 | III | III | III | III | III | III | III |
51 | 0.64 | 24.40 | 6.31 | III | IV* | IV* | IV* | III | III | III |
52 | 0.40 | 15.00 | 7.08 | III | IV* | IV* | IV* | III | III | III |
53 | 0.48 | 24.00 | 5.10 | III | III | III | III | III | III | III |
54 | 0.61 | 24.00 | 5.10 | III | III | III | III | III | III | III |
55 | 0.70 | 11.70 | 2.78 | III | IV* | IV* | III | III | III | III |
56 | 0.83 | 28.90 | 3.20 | III | II* | II* | II* | III | III | III |
57 | 0.74 | 28.90 | 3.20 | III | II* | II* | III | III | III | III |
58 | 0.79 | 22.00 | 2.00 | III | III | III | III | III | III | III |
59 | 0.84 | 19.70 | 2.30 | III | IV* | IV* | III | III | III | III |
60 | 0.38 | 21.70 | 5.00 | III | III | III | III | III | III | III |
61 | 0.56 | 9.74 | 7.27 | III | III | III | III | III | III | III |
62 | 0.52 | 21.20 | 5.50 | III | III | III | III | III | III | III |
63 | 0.60 | 28.30 | 3.40 | III | III | III | III | III | III | III |
64 | 0.53 | 21.00 | 3.60 | III | III | III | III | III | III | III |
65 | 0.66 | 21.50 | 4.10 | III | III | III | III | III | III | III |
66 | 0.52 | 17.80 | 4.30 | III | III | III | III | III | III | III |
67 | 0.57 | 25.60 | 3.80 | III | III | III | III | III | III | III |
68 | 0.61 | 25.60 | 3.70 | III | III | III | III | III | III | III |
69 | 0.56 | 29.20 | 4.80 | III | III | III | III | III | III | III |
70 | 0.49 | 49.50 | 4.70 | III | III | III | III | III | III | III |
71 | 0.47 | 55.00 | 5.00 | III | II* | I* | III | II* | II* | III |
72 | 0.61 | 25.00 | 3.70 | III | III | III | III | III | III | III |
73 | 0.55 | 31.30 | 4.60 | III | III | III | III | III | III | III |
74 | 0.50 | 50.90 | 5.20 | III | I* | I* | III | II* | II* | III |
75 | 0.69 | 16.87 | 3.41 | III | III | III | III | III | III | III |
76 | 0.54 | 12.20 | 4.89 | III | III | III | III | III | III | III |
77 | 0.47 | 16.50 | 5.52 | III | IV* | III | III | III | III | III |
78 | 0.52 | 18.60 | 4.20 | III | III | III | III | III | III | III |
79 | 0.55 | 11.10 | 4.00 | III | III | III | III | III | III | III |
80 | 0.56 | 16.30 | 3.30 | III | III | III | III | III | III | III |
81 | 0.32 | 21.70 | 5.00 | III | III | III | III | III | III | III |
82 | 0.28 | 9.50 | 6.10 | III | IV* | IV* | IV* | III | III | III |
83 | 0.66 | 22.30 | 3.20 | III | III | III | III | III | III | III |
84 | 0.72 | 27.52 | 4.30 | III | III | III | III | III | III | III |
85 | 0.62 | 19.35 | 4.50 | III | III | III | III | III | III | III |
86 | 0.59 | 18.75 | 4.20 | III | III | III | III | III | III | III |
87 | 0.73 | 29.70 | 3.80 | III | III | III | III | III | III | III |
88 | 0.62 | 20.00 | 3.10 | III | III | III | III | III | III | III |
89 | 0.61 | 17.90 | 5.30 | III | III | III | III | III | III | III |
90 | 0.47 | 11.00 | 3.97 | III | III | III | III | III | III | III |
91 | 0.58 | 13.18 | 6.27 | IV | IV | IV | IV | IV | IV | IV |
92 | 0.77 | 17.50 | 5.50 | IV | IV | IV | IV | IV | IV | IV |
93 | 0.66 | 13.20 | 6.80 | IV | IV | IV | IV | IV | IV | III* |
94 | 0.74 | 24.40 | 6.31 | IV | IV | IV | III* | IV | IV | III* |
95 | 1.00 | 11.20 | 2.00 | IV | IV | IV | IV | IV | IV | III* |
96 | 0.72 | 13.90 | 9.10 | IV | IV | IV | IV | IV | IV | IV |
97 | 0.72 | 13.20 | 5.20 | IV | IV | IV | IV | IV | IV | III* |
98 | 0.69 | 16.55 | 5.72 | IV | IV | IV | IV | IV | IV | III* |
99 | 0.65 | 12.36 | 5.41 | IV | IV | IV | IV | IV | IV | III* |
100 | 0.71 | 32.20 | 5.50 | IV | IV | IV | IV | IV | IV | III* |
Appendix 2. Entropy weight method-fuzzy mathematical model evaluation results
No | Evaluating index | Actual grade | Evaluation results of traditional fuzzy models | Evaluation results of improved fuzzy models | ||||||
---|---|---|---|---|---|---|---|---|---|---|
σθ/σc | σc/σt | Wet | Trapezoid | Normal | Quadratic parabola | Trapezoid | Normal | Quadratic parabola | ||
1 | 0.11 | 31.23 | 7.40 | I | IV* | IV* | IV* | II* | I | I |
2 | 0.20 | 36.04 | 2.29 | I | II* | IV* | I | II* | II* | II* |
3 | 0.19 | 47.93 | 1.87 | I | I | I | I | I | I | I |
4 | 0.13 | 6.67 | 1.39 | I | I | I | I | I | I | I |
5 | 0.19 | 6.67 | 1.39 | I | I | I | I | I | I | I |
6 | 0.23 | 6.67 | 1.39 | I | I | I | I | I | I | I |
7 | 0.28 | 9.68 | 1.92 | I | I | I | I | I | I | II* |
8 | 0.11 | 27.22 | 7.00 | I | IV* | IV* | IV* | III* | III* | III* |
9 | 0.14 | 14.05 | 1.30 | I | I | II* | I | I | II* | I |
10 | 0.22 | 36.42 | 1.75 | I | I | I | I | I | I | II* |
11 | 0.31 | 42.80 | 1.80 | I | I | I | I | I | I | II* |
12 | 0.20 | 11.20 | 3.60 | I | I | I | II*– III* | I | II* | II* |
13 | 0.20 | 14.10 | 3.60 | I | I | I | II*– III* | I | II* | II* |
14 | 0.28 | 42.73 | 2.17 | I | I | I | I | I | I | II* |
15 | 0.11 | 29.40 | 2.04 | I | I | I | I | I | I | II* |
16 | 0.23 | 7.52 | 1.50 | I | I | I | I | I | I | I |
17 | 0.43 | 45.90 | 1.70 | I | I | I | II* | I | II* | II* |
18 | 0.22 | 36.42 | 1.75 | I | I | I | I | I | I | I |
19 | 0.11 | 31.2 | 7.40 | I | IV* | IV* | IV* | II* | II* | III* |
20 | 0.40 | 15.61 | 3.50 | II | II | II | II | II | II | II |
21 | 0.44 | 13.13 | 2.12 | II | II | II | II | II | II | II |
22 | 0.37 | 24.00 | 5.10 | II | II | II | III* | II | III* | III* |
23 | 0.45 | 11.2 | 2.03 | II | II | II | II | II | II | II |
24 | 0.67 | 26.80 | 0.85 | II | I* | I* | I* | II | II | II |
25 | 0.56 | 20.40 | 2.00 | II | III* | III* | III* | III* | II | II |
26 | 0.46 | 20.40 | 2.00 | II | II | II | II | II | II | II |
27 | 0.49 | 19.70 | 2.30 | II | II | II | II | II | II | II |
28 | 0.28 | 23.60 | 4.90 | II | III* | III* | III* | II | III* | III* |
29 | 0.44 | 19.70 | 2.30 | II | II | II | II | II | II | II |
30 | 0.28 | 23.80 | 4.80 | II | III* | III* | III* | III* | II | II |
31 | 0.46 | 19.70 | 2.30 | II | II | II | II | II | II | II |
32 | 0.42 | 19.70 | 2.30 | II | II | II | II | II | II | II |
33 | 0.56 | 34.30 | 1.90 | II | III* | II | I* | II | II | II |
34 | 0.30 | 20.40 | 5.00 | II | III* | III* | IV* | III* | III* | III* |
35 | 0.35 | 22.70 | 3.31 | II | II | II | II | II | II | II |
36 | 0.45 | 14.82 | 3.10 | II | II | II | II | II | II | II |
37 | 0.41 | 30.70 | 4.30 | II | II | II | II | II | II | III* |
38 | 0.45 | 6.84 | 2.15 | II | II | II | II | II | II | II |
39 | 0.35 | 12.05 | 2.85 | II | II | II | II | II | II | II |
40 | 0.37 | 29.73 | 3.52 | II | II | II | II | II | II | II |
41 | 0.42 | 32.77 | 2.97 | II | II | II | II | II | II | II |
42 | 0.38 | 28.8 | 3.00 | II | II | II | II | II | II | II |
43 | 0.42 | 29.90 | 2.40 | II | II | II | II | II | II | II |
44 | 0.42 | 15.50 | 3.20 | II | II | II | II | II | II | II |
45 | 0.57 | 31.20 | 3.20 | II | II | II | II | II | II | III* |
46 | 0.44 | 8.98 | 4.86 | II | II | II | II | II | II | II |
47 | 0.43 | 13.98 | 7.44 | III | IV* | II* | IV* | III | III | III |
48 | 0.55 | 11.10 | 3.97 | III | III | III | III | III | III | III |
49 | 0.34 | 23.97 | 6.60 | III | IV* | IV* | IV* | III | III | III |
50 | 0.42 | 21.69 | 5.00 | III | II* | III | III | III | III | III |
51 | 0.64 | 24.40 | 6.31 | III | III | IV* | IV* | III | III | III |
52 | 0.40 | 15.00 | 7.08 | III | IV* | IV* | IV* | III | III | III |
53 | 0.48 | 24.00 | 5.10 | III | III | III | III | III | III | III |
54 | 0.61 | 24.00 | 5.10 | III | III | III | III | III | III | III |
55 | 0.70 | 11.70 | 2.78 | III | II* | II* | III | III | III | III |
56 | 0.83 | 28.90 | 3.20 | III | II* | II* | II* | III | III | III |
57 | 0.74 | 28.90 | 3.20 | III | II* | II* | II–III* | III | III | III |
58 | 0.79 | 22.00 | 2.00 | III | IV* | IV* | II* | III | III | III |
59 | 0.84 | 19.70 | 2.30 | III | IV* | IV* | IV* | III | III | III |
60 | 0.38 | 21.70 | 5.00 | III | II* | III | IV* | III | III | III |
61 | 0.56 | 9.74 | 7.27 | III | IV* | IV* | IV* | IV* | IV* | III |
62 | 0.52 | 21.20 | 5.50 | III | IV* | III | III | III | III | III |
63 | 0.60 | 28.30 | 3.40 | III | III | III | III | III | III | III |
64 | 0.53 | 21.00 | 3.60 | III | III | III | III | III | III | III |
65 | 0.66 | 21.50 | 4.10 | III | III | III | III | III | III | III |
66 | 0.52 | 17.80 | 4.30 | III | III | III | III | III | III | III |
67 | 0.57 | 25.60 | 3.80 | III | III | III | III | III | III | III |
68 | 0.61 | 25.60 | 3.70 | III | III | III | III | III | III | III |
69 | 0.56 | 29.20 | 4.80 | III | III | III | III | III | III | III |
70 | 0.49 | 49.50 | 4.70 | III | III | III | III | III | III | III |
71 | 0.47 | 55.00 | 5.00 | III | II* | III | III | III | III | III |
72 | 0.61 | 25.00 | 3.70 | III | III | III | III | III | III | III |
73 | 0.55 | 31.30 | 4.60 | III | III | III | III | III | III | III |
74 | 0.50 | 50.90 | 5.20 | III | IV* | III | III | III | III | III |
75 | 0.69 | 16.87 | 3.41 | III | III | III | III | III | III | III |
76 | 0.54 | 12.20 | 4.89 | III | III | III | III | III | III | III |
77 | 0.47 | 16.50 | 5.52 | III | IV* | IV* | III | III | III | III |
78 | 0.52 | 18.60 | 4.20 | III | III | III | III | III | III | III |
79 | 0.55 | 11.10 | 4.00 | III | III | III | III | III | III | III |
80 | 0.56 | 16.30 | 3.30 | III | III | III | III | III | III | III |
81 | 0.32 | 21.70 | 5.00 | III | III | III | III | III | III | III |
82 | 0.28 | 9.50 | 6.10 | III | IV* | IV* | IV* | IV* | III | III |
83 | 0.66 | 22.30 | 3.20 | III | III | III | III | III | III | III |
84 | 0.72 | 27.52 | 4.30 | III | III | III | III | III | III | III |
85 | 0.62 | 19.35 | 4.50 | III | III | III | III | III | III | III |
86 | 0.59 | 18.75 | 4.20 | III | III | III | III | III | III | III |
87 | 0.73 | 29.70 | 3.80 | III | III | III | III | III | III | III |
88 | 0.62 | 20.00 | 3.10 | III | III | III | III | III | III | III |
89 | 0.61 | 17.90 | 5.30 | III | III | III | III | III | III | III |
90 | 0.47 | 11.00 | 3.97 | III | III | III | III | III | III | III |
91 | 0.58 | 13.18 | 6.27 | IV | IV | IV | IV | IV | IV | III* |
92 | 0.77 | 17.50 | 5.50 | IV | IV | IV | IV | IV | IV | IV |
93 | 0.66 | 13.20 | 6.80 | IV | IV | IV | IV | IV | IV | IV |
94 | 0.74 | 24.40 | 6.31 | IV | IV | IV | IV | IV | IV | IV |
95 | 1.00 | 11.20 | 2.00 | IV | IV | IV | IV | IV | IV | IV |
96 | 0.72 | 13.90 | 9.10 | IV | IV | IV | IV | IV | IV | IV |
97 | 0.72 | 13.20 | 5.20 | IV | IV | IV | IV | IV | IV | III* |
98 | 0.69 | 16.55 | 5.72 | IV | IV | IV | IV | IV | IV | IV |
99 | 0.65 | 12.36 | 5.41 | IV | IV | IV | IV | IV | IV | III* |
100 | 0.71 | 32.20 | 5.50 | IV | IV | IV | IV | IV | IV | III* |
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
Wang, Q., Wang, C., Liu, Y. et al. Exploration and Improvement of Fuzzy Evaluation Model for Rockburst. Mining, Metallurgy & Exploration 41, 559–587 (2024). https://doi.org/10.1007/s42461-024-00933-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42461-024-00933-3