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Evaluation of Point-Pillar Stability Using a Hesitant Fuzzy GA-WDBA Approach

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Abstract

The stability of point-pillar is of crucial importance for the mines using point-pillar mining method. Instable pillars can cause the collapse of goaf roof and threaten the safety of workers. In this study, a hesitant fuzzy genetic algorithm-weighted distance-based approximation (GA-WDBA) approach is proposed to evaluate the stability of point-pillar. First, an evaluation criteria system of point-pillar stability is established. Considering that point-pillar stability is affected by both qualitative and quantitative factors, hesitant fuzzy numbers (HFNs) and crisp numbers are adopted to express criteria values. Then, the traditional GA is modified with a nonlinear programming algorithm to improve its local search ability and calculate criteria weights. Afterwards, the traditional WDBA method is extended with HFNs to solve those evaluation problems with hybrid decision information. Finally, the proposed methodology is applied to evaluate the point-pillar stability in Xinli mining district. The effectiveness and advantages of the methodology are discussed based on the comparison of multiple approaches. The results indicate that the proposed hesitant fuzzy GA-WDBA approach is reasonable and efficient for the evaluation of point-pillar stability, and can provide a reference for the risk management of point-pillar stability.

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References

  1. Mortazavi, A., Hassani, F.P., Shabani, M.: A numerical investigation of rock pillar failure mechanism in underground openings. Comput. Geotech. 36(5), 691–697 (2009)

    Article  Google Scholar 

  2. Liang, W.Z., Luo, S.Z., Zhao, G.Y., Wu, H.: Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics 8(5), 765 (2020)

    Article  Google Scholar 

  3. Wang, J.A., Shang, X.C., Ma, H.T.: Investigation of catastrophic ground collapse in Xingtai gypsum mines in China. Int. J. Rock Mech. Min. Sci. 45(8), 1480–1499 (2008)

    Article  Google Scholar 

  4. Esterhuizen, G.S., Dolinar, D.R., Ellenberger, J.L.: Pillar strength in underground stone mines in the United States. Int. J. Rock Mech. Min. Sci. 48(1), 42–50 (2011)

    Article  Google Scholar 

  5. Lunder, P. J.: Hard rock pillar strength estimation an applied empirical approach. Doctoral dissertation, University of British Columbia, Vancouver (1994)

  6. Jaiswal, A., Sharma, S.K., Shrivastva, B.K.: Numerical modeling study of asymmetry in the induced stresses over coal mine pillars with advancement of the goaf line. Int. J. Rock Mech. Min. Sci. 5(41), 859–864 (2004)

    Article  Google Scholar 

  7. Elmo, D., Stead, D.: An integrated numerical modelling–discrete fracture network approach applied to the characterisation of rock mass strength of naturally fractured pillars. Rock Mech. Rock Eng. 43(1), 3–19 (2010)

    Article  Google Scholar 

  8. Tawadrous, A.S., Katsabanis, P.D.: Prediction of surface crown pillar stability using artificial neural networks. Int. J. Numer. Anal. Meth. Geomech. 31(7), 917–931 (2007)

    Article  MATH  Google Scholar 

  9. Ding, H.X., Li, G.H., Dong, X., Lin, Y.: Prediction of pillar stability for underground mines using the stochastic gradient boosting technique. IEEE Access 6, 69253–69264 (2018)

    Article  Google Scholar 

  10. Wattimena, R.K.: Predicting the stability of hard rock pillars using multinomial logistic regression. Int. J. Rock Mech. Min. Sci. 100(71), 33–40 (2014)

    Article  Google Scholar 

  11. Ghasemi, E., Ataei, M., Shahriar, K.: An intelligent approach to predict pillar sizing in designing room and pillar coal mines. Int. J. Rock Mech. Min. Sci. 65, 86–95 (2014)

    Article  Google Scholar 

  12. Liang, W.Z., Sari, A., Zhao, G.Y., McKinnon, S.D., Wu, H.: Short-term rockburst risk prediction using ensemble learning methods. Nat. Hazards 104(2), 1923–1946 (2020)

    Article  Google Scholar 

  13. Luo, S., Pedrycz, W., Xing, L.: Selection of data products: a hybrid AFSA-MABAC approach. Int. J. Mach. Learn. Cybernet. 15, 1–19 (2021)

    Google Scholar 

  14. Akram, M., Ilyas, F., Al-Kenani, A.N.: Two-phase group decision-aiding system using ELECTRE III method in Pythagorean fuzzy environment. Arab. J. Sci. Eng. 46(4), 3549–3566 (2021)

    Article  Google Scholar 

  15. Liang, W.Z., Dai, B., Zhao, G.Y., Wu, H.: Assessing the performance of green mines via a hesitant fuzzy ORESTE–QUALIFLEX method. Mathematics 7(9), 788 (2019)

    Article  Google Scholar 

  16. Luo, S., Xing, L.: Neutrosophic game pricing methods with risk aversion for pricing of data products. Expert Syst. 15, 12697 (2021)

    Google Scholar 

  17. Torra, V., & Narukawa, Y.: On hesitant fuzzy sets and decision. In the 18th IEEE international conference on fuzzy systems, Jeju Island, Kore, pp. 1378–1382. (2009)

  18. Liang, W.Z., Zhao, G.Y., Wang, X., Zhao, J., Ma, C.D.: Assessing the rockburst risk for deep shafts via distance-based multi-criteria decision making approaches with hesitant fuzzy information. Eng. Geol. 14, 105211 (2019)

    Article  Google Scholar 

  19. Mishra, A.R., Rani, P., Krishankumar, R., Ravichandran, K.S., Kar, S.: An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of Coronavirus Disease 2019 (COVID-19). Appl. Soft Comput. 103, 107155 (2021)

    Article  Google Scholar 

  20. Xu, Z.S., Zhang, X.L.: Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowl.-Based Syst. 52, 53–64 (2013)

    Article  Google Scholar 

  21. Liao, H.C., Xu, Z.S.: A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optim. Decis. Making 12(4), 373–392 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhang, Y.X., Xu, Z.S.: Efficiency evaluation of sustainable water management using the HF-TODIM method. Int. Trans. Oper. Res. 26(2), 747–764 (2019)

    Article  MathSciNet  Google Scholar 

  23. Rao, R., Singh, D.: Weighted Euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection. Int. J. Ind. Eng. Comput. 3(3), 365–382 (2012)

    Google Scholar 

  24. Jain, D., Garg, R., Bansal, A., Saini, K.K.: Selection and ranking of E-learning websites using weighted distance-based approximation. J. Comput. Educ. 3(2), 193–207 (2016)

    Article  Google Scholar 

  25. Peng, X.D., Li, W.Q.: Algorithms for interval-valued Pythagorean fuzzy sets in emergency decision making based on multiparametric similarity measures and WDBA. IEEE Access 7, 7419–7441 (2019)

    Article  Google Scholar 

  26. Garg, R., Jain, D.: Fuzzy multi-attribute decision making evaluation of e-learning websites using FAHP, COPRAS, VIKOR WDBA. Decis. Sci. Lett. 6(4), 351–364 (2017)

    Article  Google Scholar 

  27. Al-Hawari, T., Naji, A., Alshraideh, H., Bataineh, O.: Extending the WEDBA to the fuzzy multi-criteria decision-making environment. Int. J. Comput. Appl. Technol. 59(4), 330–346 (2019)

    Article  Google Scholar 

  28. Aguarón, J., Escobar, M.T., Moreno-Jiménez, J.M.: Reducing inconsistency measured by the geometric consistency index in the analytic hierarchy process. Eur. J. Oper. Res. 288(2), 576–583 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang, L., Garg, H., Li, N.: Pythagorean fuzzy interactive Hamacher power aggregation operators for assessment of express service quality with entropy weight. Soft. Comput. 25(2), 973–993 (2021)

    Article  MATH  Google Scholar 

  30. Dong, L., Gu, X., Wu, X.L., Liao, H.C.: An improved MULTIMOORA method with combined weights and its application in assessing the innovative ability of universities. Expert. Syst. 36(2), e12362 (2019)

    Article  Google Scholar 

  31. Ahmadi, M.H., Ahmadi, M.A., Nazari, M.A., Mahian, O., Ghasempour, R.: A proposed model to predict thermal conductivity ratio of Al 2 O 3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J. Therm. Anal. Calorim. 135(1), 271–281 (2019)

    Article  Google Scholar 

  32. Sun, D.Y., Jia, Y.H., Wu, J., Chen, Z.D., Zhao, L.P.: Application of fuzzy theory in the optimization algorithm of the indicator weight for railway passenger transport safety evaluation. Intell. Decis. Technol. 12(4), 483–490 (2018)

    Article  Google Scholar 

  33. Du, J., Dong, P., Sugumaran, V., Castro-Lacouture, D.: Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model. Expert. Syst. 38(1), e12533 (2021)

    Article  Google Scholar 

  34. Kumar, K., Kumar, P., Singh, S.K.: Aerodynamic Performance Optimization of Multiple Slat Airfoil based on Multi-Objective Genetic Algorithm. Arab. J. Sci. Eng. 6, 1–12 (2021)

    Google Scholar 

  35. Katoch, S., Chauhan, S.S., Kumar, V.: A review on genetic algorithm: past, present, and future. Multimedia Tools Appl. 80(5), 8091–8126 (2021)

    Article  Google Scholar 

  36. Garg, H., Arora, R.: A nonlinear-programming methodology for multi-attribute decision-making problem with interval-valued intuitionistic fuzzy soft sets information. Appl. Intell. 48(8), 2031–2046 (2018)

    Article  Google Scholar 

  37. Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539 (2010)

    MATH  Google Scholar 

  38. Xu, Z.S., Xia, M.M.: On distance and correlation measures of hesitant fuzzy information. Int. J. Intell. Syst. 26(5), 410–425 (2011)

    Article  MATH  Google Scholar 

  39. Ghasemi, E., Ataei, M., Shahriar, K.: Prediction of global stability in room and pillar coal mines. Nat. Hazards 72(2), 405–422 (2014)

    Article  Google Scholar 

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Acknowledgements

This work was supported by Excellent Youth Project of Hunan Provincial Education Department (21B0062), National Key Research and Development Program of China (2018YFC0604606), and National Natural Science Foundation of China (71901226). The first author is supported by China Scholarship Council (201906370137).

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Authors and Affiliations

  1. College of Tourism, Hunan Normal University, Changsha, 410081, China

    Suizhi Luo & Jing Wang

  2. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Weizhang Liang & Guoyan Zhao

Authors
  1. Suizhi Luo
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  2. Weizhang Liang
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  3. Guoyan Zhao
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  4. Jing Wang
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Correspondence to Weizhang Liang.

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Luo, S., Liang, W., Zhao, G. et al. Evaluation of Point-Pillar Stability Using a Hesitant Fuzzy GA-WDBA Approach. Int. J. Fuzzy Syst. 24, 3702–3714 (2022). https://doi.org/10.1007/s40815-022-01355-3

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  • DOI: https://doi.org/10.1007/s40815-022-01355-3

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