Advertisement

Modification of rock mass rating system using soft computing techniques

  • Hima Nikafshan RadEmail author
  • Zakaria Jalali
Original Article
  • 27 Downloads

Abstract

Classification systems such as rock mass rating (RMR) are used to evaluate rock mass quality. This paper intended to evaluate RMR based on a fuzzy clustering algorithm to improve linguistic and empirical criteria for the RMR classification system. In the proposed algorithm, membership functions were first extracted for each RMR parameter based on the questionnaires filled out by experts. RMR clustering algorithm was determined by considering the percent importance of each parameter in the RMR classification system. In all implementation stages of the proposed algorithm, no empirical judgment was made in determining the classification classes in the RMR system. According to the obtained results, the proposed algorithm is a powerful tool to modify the rock mass rating system and can be generalized for future research.

Keywords

RMR system based on continuous rating Multi-objective optimization Genetic algorithm Fuzzy clustering algorithm 

Notes

References

  1. 1.
    Massart DL (1982) Extraction of information from large data sets by pattern recognitionGewinnung von Information aus groen Datenmengen mit Hilfe der Strukturerkennung. Fresenius’ Zeitschrift fr analytische Chemie 311(4):318–318CrossRefGoogle Scholar
  2. 2.
    Shumway RH (1987) Statistics and Data Analysis in Geology. Technometrics 29(4):492.  https://doi.org/10.1080/00401706.1987.10488290 CrossRefGoogle Scholar
  3. 3.
    Demicco RV, Klir GJ (eds) (2003) Fuzzy logic in geology. Elsevier, AmsterdamGoogle Scholar
  4. 4.
    Bezdek JC, Hathaway RJ, Sabin MJ, Tucker WT (1987) Convergence theory for fuzzy c-means: counterexamples and repairs. IEEE Trans Syst Man Cybern 17(5):873–877CrossRefGoogle Scholar
  5. 5.
    Bezdek JC (1981) Objective function clustering. Pattern recognition with fuzzy objective function algorithms. Springer, Boston, MA, pp 43–93CrossRefGoogle Scholar
  6. 6.
    Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127MathSciNetCrossRefGoogle Scholar
  7. 7.
    Dunn JC (1973) A fuzzy relative of the ISODATA process and Its use in detecting compact well-separated clusters. J Cybern 3(3):32–57.  https://doi.org/10.1080/01969727308546046 MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, AmsterdamzbMATHGoogle Scholar
  9. 9.
    Rad HN, Hasanipanah M, Rezaei M, Eghlim AL (2018) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput 34(4):709717CrossRefGoogle Scholar
  10. 10.
    Rad HN, Jalali Z, Jalalifar H (2015) Prediction of rock mass rating system based on continuous functions using Chaos–ANFIS model. Int J Rock Mech Min Sci 73:1–9CrossRefGoogle Scholar
  11. 11.
    Shahnazar A, Rad HN, Hasanipanah M, Tahir MM, Armaghani DJ, Ghoroqi M (2017) A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environ Earth Sci 76(15):527CrossRefGoogle Scholar
  12. 12.
    AminShokravi A, Eskandar H, Derakhsh AM, Rad HN, Ghanadi A (2018) The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting. Eng Comput 34(2):277–285CrossRefGoogle Scholar
  13. 13.
    Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MMD (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050CrossRefGoogle Scholar
  14. 14.
    Hasanipanah M, Armaghani DJ, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75(9):808CrossRefGoogle Scholar
  15. 15.
    Keshtegar B, Hasanipanah M, Bakhshayeshi I, Sarafraz ME (2019) A novel nonlinear modeling for the prediction of blast induced airblast using a modified conjugate FR method. Measurement 131:3541CrossRefGoogle Scholar
  16. 16.
    Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22(78):16371643Google Scholar
  17. 17.
    Hasanipanah M, Shirani Faradonbeh R, Bakhshandeh Amnieh H, Jahed Armaghani D, Monjezi M (2017) Forecasting blast induced ground vibration developing a CART model. Eng Comput 33(2):307316Google Scholar
  18. 18.
    Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705715CrossRefGoogle Scholar
  19. 19.
    Bieniawski ZT, Bieniawski ZT (1989) Engineering rock mass classifications: a complete manual for engineers and geologists in mining, civil, and petroleum engineering. Wiley, New YorkGoogle Scholar
  20. 20.
    Mukhopadhyay A, Maulik U, Bandyopadhyay S, Coello CAC (2014) Survey of multiobjective evolutionary algorithms for data mining: part II. IEEE Trans Evol Comput 18(1):20–35CrossRefGoogle Scholar
  21. 21.
    Casillas J, Cordn O, Del Jesus MJ, Herrera F (2005) Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans Fuzzy Syst 13(1):13–29CrossRefGoogle Scholar
  22. 22.
    AMEC Mining Engineering Report (2008) Geology model and model update of anomalies B and C north of Sangan iron mine project. DP01R3-BB11-C0000-AS001Rev.1, August 2008Google Scholar
  23. 23.
    AMEC Mining Engineering Report (2008) B and C north anomalies of Sangan iron mine project. DP01R3-BB11-C0000-AC001Rev.1, April 2008Google Scholar
  24. 24.
    BHP Mine Study Geotechnical Engineering Report (1992) Orebody B, joint venture-Sangan. TJB:LE-01910,BA:E236/100, August 1992Google Scholar
  25. 25.
    BHP Mine Study Geotechnical Engineering Report (1992) Orebody B and C north, joint venture-Sangan. TJB:LE-00828,BA:E236/100, May 1992Google Scholar
  26. 26.
    Bellman RE, Zadeh LA (1970) Decision-making in a fuzzy environment. Manag Sci 17(4):B-141MathSciNetCrossRefGoogle Scholar
  27. 27.
    Juang CH, Lee DH (1990) Rock mass classification using fuzzy sets. In: Asian geotechnical conference, Chinese Institute of Civil and Hydraulic Engineering, Taipei, Taiwan, pp 309–314Google Scholar
  28. 28.
    Dong WM, Wong FS (1987) Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets Syst 21(2):183–199MathSciNetCrossRefGoogle Scholar
  29. 29.
    Habibagahi G, Katebi S (1996) Rock mass classification using fuzzy sets. Iran J Sci Technol Trans B 2:​273-284Google Scholar
  30. 30.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference very large data bases VLDB, vol 1215. Morgan Kaufmann Publishers Inc., San Francisco, pp 487–499Google Scholar
  31. 31.
    Hong TP, Kuo CS, Chi SC (2001) Trade-off between computation time and number of rules for fuzzy mining from quantitative data. Int J Uncertain Fuzziness Knowl Based Syst 9(05):587–604CrossRefGoogle Scholar
  32. 32.
    Romsaiyud W, Premchaiswadi W (2012) Applying mining fuzzy sequential patterns technique to predict the leadership in social networks. In: 2011 9th international conference on ICT and knowledge engineering (ICT and knowledge engineering). IEEE, pp 134–137Google Scholar
  33. 33.
    Homaifar A (1993) A new approach on the traveling salesman problem by genetic algorithms. In: Proceedings of the 5th ICGA.Google Scholar
  34. 34.
    Alcala R, Alcala-Fdez J, Gacto MJ, Herrera F (2007) Genetic learning of membership functions for mining fuzzy association rules. The IEEE international conference on fuzzy systems, pp 1–6Google Scholar
  35. 35.
    Gautam P, Khare N, Pardasani KR (2010) A model for mining multilevel fuzzy association rule in database. CoRR abs/1001.3488 Google Scholar
  36. 36.
    Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern 16(1):122–128CrossRefGoogle Scholar
  37. 37.
    Cant-Paz E (1998) A survey of parallel genetic algorithms. Calculateurs Paralleles Reseaux et Systems Repartis 10(2):141–171Google Scholar
  38. 38.
    Hong TP, Lee YC, Wu MT (2014) An effective parallel approach for genetic-fuzzy data mining. Expert Syst Appl 41(2):655–662CrossRefGoogle Scholar
  39. 39.
    Hong TP, Chen CH, Wu YL, Lee YC (2006) A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Comput 10(11):1091–1101CrossRefGoogle Scholar
  40. 40.
    Herrera F, Lozano M, Verdegay JL (1997) Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst 92(1):21–30CrossRefGoogle Scholar
  41. 41.
    Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678CrossRefGoogle Scholar
  42. 42.
    Ruspini EH (1969) A new approach to clustering. Inf Control 15(1):22–32CrossRefGoogle Scholar
  43. 43.
    Bezdek JC, Dunn JC (1975) Optimal fuzzy partitions: a heuristic for estimating the parameters in a mixture of normal distributions. IEEE Trans Comput 100(8):835–838CrossRefGoogle Scholar
  44. 44.
    Chiu SL (1994) A cluster estimation method with extension to fuzzy model identification. Proc IEEE int conf fuzzy syst 2:1240–1245Google Scholar
  45. 45.
    Dave RN (1990) Fuzzy shell-clustering and applications to circle detection in digital images. Int J Gen Syst 16(4):343–355MathSciNetCrossRefGoogle Scholar
  46. 46.
    Hathaway RJ, Bezdek JC, Hu Y (2000) Generalized fuzzy c-means clustering strategies using L/sub p/norm distances. IEEE Trans Fuzzy Syst 8(5):576–582CrossRefGoogle Scholar
  47. 47.
    Gan G, Wu J, Yang Z (2009) A genetic fuzzy k-modes algorithm for clustering categorical data. Expert Syst Appl 36(2):1615–1620CrossRefGoogle Scholar
  48. 48.
    Mukhopadhyay A, Maulik U, Bandyopadhyay S (2009) Multiobjective genetic algorithm-based fuzzy clustering of categorical attributes. IEEE Trans Evol Comput 13(5):991–1005CrossRefGoogle Scholar
  49. 49.
    Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer ScienceTabari University of BabolBabolIran
  2. 2.Department of Mining Engineering, Higher Educational Complex of ZarandShahid Bahonar University of KermanKermanIran

Personalised recommendations