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An interval fuzzy comprehensive assessment method for rock burst in underground caverns and its engineering application

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Abstract

Rock burst is a dynamic process involving the sudden release of elastic energy accumulated in overstressed rocks and coal masses during underground excavations, capable of causing a great number of fatalities, failure of supporting structures, and damage to equipment. To minimize the risk, a fuzzy comprehensive assessment model was established to predict and evaluate rock burst in underground caverns, and some indices were selected and analyzed. The result vectors and weight vectors were expressed by interval numbers and the membership degrees were determined by a sigmoid membership function. In addition, synthetic weight was obtained by combining subjective weight with objective weight, and final vectors were categorized into different levels through possibility ranking analysis. Finally, the proposed method was applied to a practical case, the Jiangbian hydropower station in China, for further verification. Considering that some indices were conceptually dependent on one another, the multicollinearity of these indices was evaluated according to results based on different indicator systems. The evaluation results showed a high consistency with the actual situation. Moreover, the indicator system, which made full use of the obtained data, had more reliable results than other indicator systems. The proposed fuzzy comprehensive assessment method provides valuable guidance for rock burst assessment.

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References

  • Adoko AC, Gokceoglu C, Wu L, Zuo QJ (2013) Knowledge-based and data-driven fuzzy modeling for rockburst prediction. Int J Rock Mech Min 61(4):86–95

    Article  Google Scholar 

  • Aliyu MM, Shang J, Murphy W, Lawrence JA, Collier R, Kong F, Zhao Z (2019) Assessing the uniaxial compressive strength of extremely hard cryptocrystalline flint. Int J Rock Mech Min Sci 113:310–321

  • Cai M (2013) Principles of rock support in burst-prone ground. Tunn Undergr Space Technol 36:46–56

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Chen SY, Guo Y (2005) Model of variable fuzzy sets and its application to classification prediction of rockburst. Chin J Rock Mech Eng 24:4603–4609

    Google Scholar 

  • Chen BR, Feng XT, Li QP, Luo RZ, Li S (2015) Rock burst intensity classification based on the radiated energy with damage intensity at Jinping II hydropower station, China. Rock Mech Rock Eng 48(1):289–303

    Article  Google Scholar 

  • Cook NGW, Hoek E, Pretorius JP, Ortlepp WD, Salamon MDG (2016) Rock mechanics applied to study of rockburst. J South Afr Inst Min Metall 66(10):435–528

    Google Scholar 

  • Du K, Tao M, Li XB, Zhou J (2016) Experimental study of slabbing and rockburst induced by true-triaxial unloading and local dynamic disturbance. Rock Mech Rock Eng 49(9):3437–3453

  • Gong J, Hu NL, Cui X, Wang XX (2014) Rock burst tendency prediction based on AHP-TOPSIS evaluation model. Chin J Rock Mech Eng 33(7):1442–1448

    Google Scholar 

  • Guo YH, Jiang F (2010) Application of comprehensive fuzzy evaluation in burst-proneness risk of coal seam. Int Conf Future Inf Technol Manag Eng IEEE 38(6):404–407

    Google Scholar 

  • Hao J, Shi KB, Wang XL et al (2016) Application of cloud model to rating of rockburst based on rough set of FCM algorithm. Rock Soil Mech 37(3):859–866

    Google Scholar 

  • He MC, Miao JL, Feng JL (2010) Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions. Int J Rock Mech Min 47(2):286–298

    Article  Google Scholar 

  • Hu J, Li S, Li L, Shi S, Zhou Z, Liu H, He P (2018) Field, experimental, and numerical investigation of a rockfall above a tunnel portal in southwestern China. Bull Eng Geol Environ 77(4):1365-1382

  • Huang BX, Liu CY, Fu JH, Guan H (2011) Hydraulic fracturing after water pressure control blasting for increased fracturing. Int J Rock Mech Min 48(6):976–983

    Article  Google Scholar 

  • Huang YR, Mao JX, Lin CY, Li SL, Hu JY (2014) The multi-criteria evaluation of rockburst proneness on deep buried large tunnel. J Railw Eng Soc 31(7):89–94

    Google Scholar 

  • Jia YP, Lv Q, Shang YQ, Du LL, Zhi MM (2014) Rockburst prediction based on rough set and ideal point method. J Zhejiang Univ 48(3):498–503

    Google Scholar 

  • Jiang Q, Feng XT, Xiang TB, Su GS (2010) Rockburst characteristics and numerical simulation based on a new energy index: a case study of a tunnel at 2500m depth. Bull Eng Geol Environ 69(3):381–388

    Article  Google Scholar 

  • Kidybiński A (1981) Bursting liability indices of coal. Int J Rock Mech Min 18(4):295–304

    Article  Google Scholar 

  • Kong F, Shang J (2018) A validation study for the estimation of uniaxial compressive strength based on index tests. Rock Mech Rock Eng 51(7):2289–2297

  • Li QD, Han GZ, Zeng WY, Yu XC (2016) Ranking method of interval numbers based on Boolean matrix. Control and Decision 31(4):629–634

  • Li N, Feng X, Jimenez R (2017) Predicting rock burst hazard with incomplete data using Bayesian networks. Tunn Undergr Space Technol 61:61–70

    Article  Google Scholar 

  • Liu Q, Chen X, Wu Z, Liu X (2013a) Determination method of nonlinear membership function based on the fuzzy density means cluster. Res J Appl Sci Eng Technol 5(8):2527–2530

    Article  Google Scholar 

  • Liu ZB, Shao JF, Xu WY, Meng YD (2013b) Prediction of rock burst classification using the technique of cloud models with attribution weight. Nat Hazards 68(2):549–568

    Article  Google Scholar 

  • Miao SJ, Cai MF, Guo QF, Huang ZJ (2016) Rock burst prediction based on in-situ stress and energy accumulation theory. Int J Rock Mech Min 83:86–94

    Article  Google Scholar 

  • Peng Q, Qian A, Xiao Y (2010) Research on prediction system for rockburst based on artificial intelligence application methods. J Sichuan Univ Eng Sci Ed 42(2):18–24

    Google Scholar 

  • Peng YH, Peng K, Zhou J, Liu ZX (2014) Prediction of classification of rock burst risk based on genetic algorithms with SVM. Appl Mech Mater 628:383–389

    Article  Google Scholar 

  • Qiu D, Zhang L, Li S, Zhang D (2011) Application of ideal point method in Rockburst prediction based on weight back analysis method. GeoHunan Int Conf. https://doi.org/10.1061/47631(410)31

  • Russenes BF (1974) Analysis of rock spalling for tunnels in steep valley sides (in Norwegian) Master Thesis of Science, Norwegian Institute of Technology, Trondheim

  • Saaty TL, Vargas LG (1987) Uncertainty and rank order in the analytic hierarchy process. Eur J Oper Res 32(1):107–117

    Article  Google Scholar 

  • Shan ZG, Yan P (2010) Management of rock burst during excavation of the deep tunnels in Jinping II hydropower station. Bull Eng Geol Environ 69(3):353–363

    Article  Google Scholar 

  • Shang YJ, Zhang JJ, Fu BJ (2013) Analyses of three parameters for strain mode rockburst and expression of rockburst potential. Chin J Rock Mech Eng 32(8):1520–1527

    Google Scholar 

  • Sun J, Wu J, (2014) Optimization study on interval number judgment matrix weight vector based on immune evolution algorithm. International Journal of Security and Its Applications 8(3):355–362

  • Tang LZ, Wang WX (2002) A new index of rock burst proneness. Chin J Rock Mech Eng 21(6):874–878

    Google Scholar 

  • Turchaninov A, Markov GA, Gzovsky MV et al (1972) State of stress in the upper part of the Earth’s crust based on direct measurements in mines and on tectonophysical and seismological studies. Phys Earth Planet Inter 6:229–234

    Article  Google Scholar 

  • Wang C, Peng W (2014) Rockburst prediction based on electromagnetic radiation technology and its application. Adv Mater Res 1010:1564–1567

    Article  Google Scholar 

  • Wang C, Lin L, Liu J (2012) Uncertainty weight generation approach based on uncertainty comparison matrices. Appl Math 3(5):499–507

    Article  Google Scholar 

  • Wang XT, Li SC, Xu ZH, Hu J, Pan DD, Xue YG (2018) Risk assessment of water inrush in karst tunnels excavation based on normal cloud model. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-018-1294-6

  • Wang XT, Li SC, Xu ZH, Lin P, Hu J, Wang WY (2019) Analysis of factors influencing floor water inrush in coal mines: a nonlinear fuzzy interval assessment method. Mine Water Environ. https://doi.org/10.1007/s10230-018-00578-x

  • Xue YG, Li ZQ, Li SC, Qiu DH, Tao YF et al (2017) Prediction of rock burst in underground caverns based on rough set and extensible comprehensive evaluation. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1117-1

  • Yang J, Wu X (2005) Comprehensive forecasting method for estimating rock burst. Chin J Rock Mech Eng 24(3):411–416

    Google Scholar 

  • Yu HC, Liu HN, Lu XS, Liu HD (2009) Prediction method of rock burst proneness based on rough set and genetic algorithm. J Coal Sci Eng 15(4):367–373

    Article  Google Scholar 

  • Zhang C, Zhou H, Qiu S, Wu W (2012) A top pilot tunnel preconditioning method for the prevention of extremely intense rockburst in deep tunnels excavated by TBMS. Rock Mech Rock Eng 45(3):289–309

    Article  Google Scholar 

  • Zhang Y, Xiao PX, Chen LJ (2014) Method of layout design based on ratio of rock strength to in-situ stress for large underground caverns. Chin J Rock Mech Eng 33(11):2314–2331

    Google Scholar 

  • Zhang Y, Liang P, Liu X, Liu S, Tian B (2015) Experimental study on precursor of rock burst based on acoustic emission signal dominant-frequency and entropy. Chin J Rock Mech Eng 34:2959–2967

    Google Scholar 

  • Zhao S, Chai LH (2015) A new assessment approach for urban ecosystem health basing on maximum information entropy method. Stoch Env Res Risk A 29(6):1601–1613

    Article  Google Scholar 

  • Zhou KP, Lin Y, Deng HW, Li JL, Liu CJ (2016) Prediction of rock burst classification using cloud model with entropy weight. Trans Nonferrous Metal Soc China 26(7):1995–2002

    Article  Google Scholar 

Download references

Acknowledgments

Much of the work presented in this paper was supported by the National Natural Science Foundation of China (Grant Nos. 51879153, 51422904, 51379112, 41877239) and the Fundamental Research Funds of Shandong University (Grant Nos. 2017JC001, 2017JC002).

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Authors and Affiliations

  1. Geotechnical & Structural Engineering Research Center, Shandong University, Jinan, 250061, Shandong, China

    Xintong Wang, Shucai Li, Zhenhao Xu, Yiguo Xue, Jie Hu & Zhiqiang Li

  2. School of Qilu Transportation, Shandong University, Jinan, 250061, Shandong, China

    Zhenhao Xu

  3. Chinese Academy of Geological Sciences, Beijing, 100037, China

    Zhenhao Xu

  4. School of Civil Engineering, Shandong University, Jinan, 250061, Shandong, China

    Bo Zhang

Authors
  1. Xintong Wang
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  2. Shucai Li
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  3. Zhenhao Xu
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  4. Yiguo Xue
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  5. Jie Hu
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  6. Zhiqiang Li
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  7. Bo Zhang
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Corresponding authors

Correspondence to Zhenhao Xu or Bo Zhang.

Appendix

Appendix

For the interval number X = [a, b] and Y = [c, d], the binary arithmetic rule to which the interval number should be followed (Sun and Wu 2014) are shown in the following formula:

$$ {\displaystyle \begin{array}{l}\left[a,b\right]+\left[c,d\right]=\left[a+c,b+d\right]\\ {}\left[a,b\right]-\left[c,d\right]=\left[a-d,b-c\right]\\ {}\left[a,b\right]\times \left[c,d\right]=\left[\min \left\{ ac, ad, bc, bd\right\},\max \left\{ ac, ad, bc, bd\right\}\right]\\ {}\left[a,b\right]\div \left[c,d\right]=\left[a,b\right]\times \left[\frac{1}{c},\frac{1}{d}\right]\\ {}e\times \left[a,b\right]=\Big\{\begin{array}{cc}\left[ ea, eb\right]& a>0\\ {}\left[ eb, ea\right]& a<0\end{array}\\ {}{c}^X=\left[{c}^a,{c}^b\right]\\ {}{X}^n=\left[{(a)}^n,{(b)}^n\right]\\ {}\frac{1}{\left[a,b\right]}=\left[\frac{1}{a},\frac{1}{b}\right],0\notin \left[a,b\right]\end{array}} $$

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Wang, X., Li, S., Xu, Z. et al. An interval fuzzy comprehensive assessment method for rock burst in underground caverns and its engineering application. Bull Eng Geol Environ 78, 5161–5176 (2019). https://doi.org/10.1007/s10064-018-01453-3

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  • DOI: https://doi.org/10.1007/s10064-018-01453-3

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