Skip to main content
SpringerLink
Log in

A Review: Applications of Fuzzy Theory in Rock Engineering

  • State of the Art/Practice Paper
  • Published:
Indian Geotechnical Journal Aims and scope Submit manuscript

Abstract

One of the sub-disciplines of geo-engineering is rock engineering, which studies the behavior of rocks against internal and external factors. Fuzzy theory can be used to solve many geotechnical problems due to the uncertainty of geotechnical data and environmental characteristics. This research review aims to briefly examine the application of the fuzzy approach in the field of Rock engineering. The origin of the research in 1985 and the scope of its applications recorded in international journals were considered. The articles were reviewed based on approaches such as the year of publication, the author's nationality, the field of application, and the credibility of the published journal. The applications of fuzzy theory have been studied in seven groups, such as mechanized excavation, underground structures design, rock slope stability, rock mass properties, rock mass engineering classification, and other secondary fields of classification, and the most used tools in each stage were introduced. The research results show an attitude toward applying fuzzy theory in rock mechanics and suggest potential directions for further work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn Undergr Space Technol 23(5):600–608

    Article  Google Scholar 

  2. Kucuk K, Aksoy CO, Basarir H, Onargan T, Genis M, Ozacar V (2011) Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling. Tunn Undergr Space Technol 26(1):38–45

    Article  Google Scholar 

  3. Iphar M (2012) ANN and ANFIS performance prediction models for hydraulic impact hammers. Tunn Undergr Space Technol 27(1):23–29

    Article  Google Scholar 

  4. Den Hartog MH, Babuška R, Deketh HJR, Grima MA, Verhoef PNW, Verbruggen HB (1997) Knowledge-based fuzzy model for performance prediction of a rock-cutting trencher. Int J Approx Reason 16(1):43–66

    Article  Google Scholar 

  5. Grima MA, Verhoef PNW (1999) Forecasting rock trencher performance using fuzzy logic. Int J Rock Mech Min Sci 36(4):413–432

    Google Scholar 

  6. Grima MA, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn Undergr Space Technol 15(3):259–269

    Article  Google Scholar 

  7. Garrouch AA, Lababidi HMS (2001) Development of an expert system for underbalanced drilling using fuzzy logic. J Pet Sci Eng 31(1):23–39

    Article  CAS  Google Scholar 

  8. Mikaeil R, Naghadehi MZ, Sereshki F (2009) Multifactorial fuzzy approach to the penetrability classification of TBM in hard rock conditions. Tunn Undergr Space Technol 24(5):500–505

    Article  Google Scholar 

  9. Acaroglu O (2011) Prediction of thrust and torque requirements of TBMs with fuzzy logic models. Tunn Undergr Sp Technol 26(2):267–275

    Article  Google Scholar 

  10. Yazdani-Chamzini A, Yakhchali SH (2012) Tunnel boring machine (TBM) selection using fuzzy multicriteria decision making methods. Tunn Undergr Space Technol 30:194–204

    Article  Google Scholar 

  11. Saeidi O, Torabi SR, Ataei M (2014) Prediction of the rock mass diggability index by using fuzzy clustering-based, ANN and multiple regression methods. Rock Mech rock Eng 47(2):717–732

    Article  ADS  Google Scholar 

  12. Ghasemi E, Yagiz S, Ataei M (2014) Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic. Bull Eng Geol Environ 73(1):23–35

    Article  Google Scholar 

  13. Huang H, Gong W, Juang CH, Khoshnevisan S (2014) Robust geotechnical design of shield-driven tunnels using fuzzy sets. Tunnel Undergr. Constr., pp 184–194

  14. Basarir H, Tutluoglu L, Karpuz C (2014) Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions. Eng Geol 173:1–9

    Article  Google Scholar 

  15. Rostami J (2016) Performance prediction of hard rock tunnel boring machines (TBMs) in difficult ground. Tunn Undergr Space Technol 57:173–182

    Article  Google Scholar 

  16. Sakız U, Kaya GU, Yaralı O (2021) Prediction of drilling rate index from rock strength and cerchar abrasivity index properties using fuzzy inference system. Arab J Geosci 14(5):1–16

    Article  Google Scholar 

  17. Afradi A, Ebrahimabadi A, Hallajian T (2022) Prediction of TBM penetration rate using fuzzy logic, particle swarm optimization and harmony search algorithm. Geotech Geol Eng 40(3):1513–1536

    Article  Google Scholar 

  18. Wenxiu L (1991) Fuzzy mathematics method for theoretical analysis of ground movements due to underground excavation. Min Sci Technol 13(1):45–56

    Article  Google Scholar 

  19. Tonon F, Mammino A, Bernardini A (2002) Multiobjective optimization under uncertainty in tunneling: application to the design of tunnel support/reinforcement with case histories. Tunn Undergr Space Technol 17(1):33–54. https://doi.org/10.1016/S0886-7798(02)00002-0

    Article  Google Scholar 

  20. Panou KD (2002) A fuzzy multicriteria evaluation system for the assessment of tunnels vis-a-vis surface roads: theoretical aspects—part I. Tunn Undergr Space Technol 17(2):195–207

    Article  Google Scholar 

  21. Yang Y, Yin J-H, Yuan J-X, Schulyer JN (2003) An expert system for selection of retaining walls and groundwater controls in deep excavation. Comput Geotech 30(8):707–719

    Article  Google Scholar 

  22. Zang CW, Huang HW, Zhang ZX (2004) Forecasting the strata condition of a long road tunnel by using fuzzy synthetic judgment. Int J Rock Mech Min Sci 41:267–272

    Article  Google Scholar 

  23. Li W, Mei S, Zai S, Zhao S, Liang X (2006) Fuzzy models for analysis of rock mass displacements due to underground mining in mountainous areas. Int J Rock Mech Min Sci 43(4):503–511

    Article  Google Scholar 

  24. Li W-X, Dai L-F, Hou X-B, Lei W (2007) Fuzzy genetic programming method for analysis of ground movements due to underground mining. Int J Rock Mech Min Sci 44(6):954–961

    Article  Google Scholar 

  25. Chen Y-L, Azzam R, Fernandez-Steeger TM, Li L (2009) Studies on construction pre-control of a connection aisle between two neighbouring tunnels in Shanghai by means of 3D FEM, neural networks and fuzzy logic. Geotech Geol Eng 27(1):155–167

    Article  Google Scholar 

  26. Al-Labadi KR, Al-Humaidi HM, Tan FH (2009) Safety assessment in tunnel grouting using fuzzy rotational and angular models. J Perform Constr Facil 23(6):423–431

    Article  Google Scholar 

  27. Harrison JP, Hudson JA (2010) Incorporating parameter variability in rock mechanics analyses: fuzzy mathematics applied to underground rock spalling. Rock Mech rock Eng 43(2):219–224

    Article  ADS  Google Scholar 

  28. Wen-Xiu L, Hai-Ning L (2009) Fuzzy system models (FSMs) for analysis of rock mass displacement caused by underground mining in soft rock strata. Expert Syst Appl 36(3):4637–4645

    Article  Google Scholar 

  29. Yin Z, Chen Y, Wang P (2010) The BPNN-fuzzy logic pre-control of an underground project in city center of Shanghai. In Advances in Neural Network Research and Applications, Springer, pp 877–884

  30. Aalianvari A, Katibeh H, Sharifzadeh M (2012) Application of fuzzy Delphi AHP method for the estimation and classification of Ghomrud tunnel from groundwater flow hazard. Arab J Geosci 5(2):275–284

    Article  Google Scholar 

  31. Pourtaghi A, Lotfollahi-Yaghin MA (2012) Wavenet ability assessment in comparison to ANN for predicting the maximum surface settlement caused by tunneling. Tunn Undergr Space Technol 28:257–271

    Article  Google Scholar 

  32. Li W-X, Liu S-J, Li J-F, Ji Z-H, Wang Q, Yin X (2013) Ground movement analysis in deep iron mine using fuzzy probability theory. Appl Math Model 37(1–2):345–356

    Article  MathSciNet  Google Scholar 

  33. Žlender B, Jelušič P, Boumezerane D (2013) The feasibility analysis of underground gas storage caverns. Eng Struct 55:16–25

    Article  Google Scholar 

  34. Lee K-H, Bang J-H, Lee I-M, Shin Y-J (2013) Use of fuzzy probability theory to assess spalling occurrence in underground openings. Int J Rock Mech Min Sci 64:60–67

    Article  Google Scholar 

  35. Khamesi H, Torabi SR, Mirzaei-Nasirabad H, Ghadiri Z (2015) Improving the performance of intelligent back analysis for tunneling using optimized fuzzy systems: case study of the Karaj Subway Line 2 in Iran. J Comput Civ Eng 29(6):5014010

    Article  Google Scholar 

  36. Fattahi H, Farsangi MAE, Shojaee S, Mansouri H (2015) Selection of a suitable method for the assessment of excavation damage zone using fuzzy AHP in Aba Saleh Almahdi tunnel, Iran. Arab J Geosci 8(5):2863–2877

    Article  Google Scholar 

  37. Mohammadi M, Hossaini MF, Mirzapour B, Hajiantilaki N (2015) Use of fuzzy set theory for minimizing overbreak in underground blasting operations–A case study of Alborz Tunnel, Iran. Int J Min Sci Technol 25(3):439–445

    Article  Google Scholar 

  38. Rao J, Xie T, Liu Y (2016) RETRACTED ARTICLE: fuzzy evaluation model for in-service karst highway tunnel structural safety. KSCE J Civ Eng 20(4):1242–1249

    Article  Google Scholar 

  39. Rafie M, Namin FS (2015) Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system. Int J Min Sci Technol 25(4):655–663

    Article  Google Scholar 

  40. Meng Q, Han L, Xiao Y, Li H, Wen S, Zhang J (2016) Numerical simulation study of the failure evolution process and failure mode of surrounding rock in deep soft rock roadways. Int J Min Sci Technol 26(2):209–221

    Article  Google Scholar 

  41. Meng G et al (2021) Risk assessment of shield tunnel construction in karst strata based on fuzzy analytic hierarchy process and cloud model. Shock Vib 2021:1–16

    CAS  Google Scholar 

  42. Kacewicz M (1987) Fuzzy slope stability method. Math Geol 19(8):757–767

    Article  Google Scholar 

  43. Juang CH, Jhi Y-Y, Lee D-H (1998) Stability analysis of existing slopes considering uncertainty. Eng Geol 49(2):111–122

    Article  Google Scholar 

  44. Dodagoudar GR, Venkatachalam G (2000) Reliability analysis of slopes using fuzzy sets theory. Comput Geotech 27(2):101–115

    Article  Google Scholar 

  45. Li WX, Mei SH (2004) Fuzzy system method for the design of a jointed rock slope. Int J Rock Mech Min Sci 41:569–574

    Article  Google Scholar 

  46. Saboya F Jr, da Glória Alves M, Pinto WD (2006) Assessment of failure susceptibility of soil slopes using fuzzy logic. Eng Geol 86(4):211–224

    Article  Google Scholar 

  47. Aksoy H, Ercanoglu M (2007) Fuzzified kinematic analysis of discontinuity-controlled rock slope instabilities. Eng Geol 89(3–4):206–219

    Article  Google Scholar 

  48. Juang CH, Lee DH, Sheu C (1992) Mapping slope failure potential using fuzzy sets. J Geotech Eng 118(3):475–494

    Article  Google Scholar 

  49. Daftaribesheli A, Ataei M, Sereshki F (2011) Assessment of rock slope stability using the fuzzy slope mass rating (FSMR) system. Appl Soft Comput 11(8):4465–4473

    Article  Google Scholar 

  50. Sezer EA, Pradhan B, Gokceoglu C (2011) Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 38(7):8208–8219

    Article  Google Scholar 

  51. Chen C, Xiao Z, Zhang G (2011) Stability assessment model for epimetamorphic rock slopes based on adaptive neuro-fuzzy inference system. Electron J Geotech Eng 16(A):93–107

    Google Scholar 

  52. Park HJ, Um J-G, Woo I, Kim JW (2012) Application of fuzzy set theory to evaluate the probability of failure in rock slopes. Eng Geol 125:92–101

    Article  Google Scholar 

  53. Cheng M-Y, Roy AFV, Chen K-L (2012) Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction. Expert Syst Appl 39(2):1737–1746

    Article  Google Scholar 

  54. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996

    Article  Google Scholar 

  55. Kayastha P (2012) Application of fuzzy logic approach for landslide susceptibility mapping in Garuwa sub-basin, East Nepal. Front Earth Sci 6(4):420–432

    Article  ADS  Google Scholar 

  56. Li W-X, Qi D-L, Zheng S-F, Ren J-C, Li J, Yin X (2015) Fuzzy mathematics model and its numerical method of stability analysis on rock slope of opencast metal mine. Appl Math Model 39(7):1784–1793

    Article  MathSciNet  Google Scholar 

  57. Marandi SM, Anvar M, Bahrami M (2016) Uncertainty analysis of safety factor of embankment built on stone column improved soft soil using fuzzy logic α-cut technique. Comput Geotech 75:135–144

    Article  Google Scholar 

  58. Guo Z et al (2017) Hazard assessment of potentially dangerous bodies within a cliff based on the Fuzzy-AHP method: a case study of the Mogao Grottoes, China. Bull Eng Geol Environ 76(3):1009–1020

    Article  Google Scholar 

  59. Azarafza M, Akgün H, Feizi-Derakhshi M-R, Azarafza M, Rahnamarad J, Derakhshani R (2020) Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method. Heliyon 6(5):e03907

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gokceoglu C (2002) A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Eng Geol 66(1–2):39–51

    Article  Google Scholar 

  61. Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17(1):61–72

    Article  Google Scholar 

  62. Zorlu K, Gokceoglu C, Sonmez H (2004) Prediction of the uniaxial compressive strength of a greywacke by fuzzy inference system. In Engineering Geology for Infrastructure Planning in Europe, Springer, 2004, pp 203–210

  63. Gokceoglu C, Yesilnacar E, Sonmez H, Kayabasi A (2004) A neuro-fuzzy model for modulus of deformation of jointed rock masses. Comput Geotech 31(5):375–383

    Article  Google Scholar 

  64. Singh TN, Sinha S, Singh VK (2007) Prediction of thermal conductivity of rock through physico-mechanical properties. Build Environ 42(1):146–155

    Article  Google Scholar 

  65. Nefeslioglu HA, Gokceoglu C, Sonmez H (2006) Indirect determination of weighted joint density (wJd) by empirical and fuzzy models: Supren (Eskisehir, Turkey) marbles. Eng Geol 85(3–4):251–269

    Article  Google Scholar 

  66. Yagiz S, Gokceoglu C (2010) Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Syst Appl 37(3):2265–2272

    Article  Google Scholar 

  67. Bashari A, Beiki M, Talebinejad A (2011) Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling. Int J Rock Mech Min Sci 48(8):1224–1234

    Article  Google Scholar 

  68. Monjezi M, Rezaei M (2011) Developing a new fuzzy model to predict burden from rock geomechanical properties. Expert Syst Appl 38(8):9266–9273

    Article  Google Scholar 

  69. Asadi M, Bagheripour MH, Eftekhari M (2013) Development of optimal fuzzy models for predicting the strength of intact rocks. Comput Geosci 54:107–112

    Article  ADS  Google Scholar 

  70. Wasantha PLP, Ranjith PG, Viete DR (2012) Constitutive models describing the influence of the geometry of partially-spanning joints on jointed rock mass strength: regression and fuzzy logic analysis of experimental data. Expert Syst Appl 39(9):7663–7672

    Article  Google Scholar 

  71. Yesiloglu-Gultekin N, Sezer EA, Gokceoglu C, Bayhan H (2013) An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents. Expert Syst Appl 40(3):921–928

    Article  Google Scholar 

  72. Mishra DA, Basu A (2013) Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng Geol 160:54–68

    Article  Google Scholar 

  73. Motamedi S, Shamshirband S, Petković D, Hashim R (2015) Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sand-cement mixture. Powder Technol 278:278–285

    Article  CAS  Google Scholar 

  74. Alemdag S, Gurocak Z, Cevik A, Cabalar AF, Gokceoglu C (2016) Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Eng Geol 203:70–82

    Article  Google Scholar 

  75. Asadi M (2016) Optimized Mamdani fuzzy models for predicting the strength of intact rocks and anisotropic rock masses. J Rock Mech Geotech Eng 8(2):218–224

    Article  Google Scholar 

  76. Fattahi H (2016) Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering. Geosci J 20(5):681–690

    Article  MathSciNet  CAS  Google Scholar 

  77. Parsajoo M, Armaghani DJ, Mohammed AS, Khari M, Jahandari S (2021) Tensile strength prediction of rock material using non-destructive tests: a comparative intelligent study. Transp Geotech 31:100652

    Article  Google Scholar 

  78. Wang Z, Li W, Chen J (2022) Application of various nonlinear models to predict the uniaxial compressive strength of weakly cemented Jurassic rocks. Nat Resour Res 31(1):371–384

    Article  Google Scholar 

  79. Nguyen VU (1985) Some fuzzy set applications in mining geomechanics. Int J Rock Mech Min Sci Geomech Abstr 22(6):369–379

    Article  Google Scholar 

  80. Nikravesh M, Aminzadeh F (2001) Mining and fusion of petroleum data with fuzzy logic and neural network agents. J Pet Sci Eng 29(3–4):221–238

    Article  CAS  Google Scholar 

  81. Kesimal A, Bascetin A (2002) Application of fuzzy multiple attribute decision making in mining operations. Miner Resour Eng 11(01):59–72

    Article  Google Scholar 

  82. Chakraborty M, Chandra MK (2005) Multicriteria decision making for optimal blending for beneficiation of coal: a fuzzy programming approach. Omega 33(5):413–418

    Article  Google Scholar 

  83. De Almeida AT (2005) Mining methods selection based on multicriteria models. Proc Appl Comput Oper Res Miner Ind, 49(4)

  84. Iphar M, Goktan RM (2006) An application of fuzzy sets to the Diggability Index Rating Method for surface mine equipment selection. Int J Rock Mech Min Sci 43(2):253–266

    Article  Google Scholar 

  85. Deb D, Choi SO (2006) Analysis of sinkhole occurrences over abandoned mines using fuzzy reasoning: a case study. Geotech Geol Eng 24(5):1243–1258

    Article  Google Scholar 

  86. Ataei M, Khalokakaei R, Hossieni M (2009) Determination of coal mine mechanization using fuzzy logic. Min Sci Technol 19(2):149–154

    Google Scholar 

  87. Monjezi M, Rezaei M, Varjani AY (2009) Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int J Rock Mech Min Sci 46(8):1273–1280

    Article  Google Scholar 

  88. Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37(3):2637–2643

    Article  Google Scholar 

  89. Azimi Y, Osanloo M, Aakbarpour-Shirazi M, Bazzazi AA (2010) Prediction of the blastability designation of rock masses using fuzzy sets. Int J Rock Mech Min Sci 47(7):1126–1140

    Article  Google Scholar 

  90. Fişne A, Kuzu C, Hüdaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174(1):461–470

    Article  PubMed  Google Scholar 

  91. Malinowska A (2011) A fuzzy inference-based approach for building damage risk assessment on mining terrains. Eng Struct 33(1):163–170

    Article  Google Scholar 

  92. Alipour A, Ashtiani M (2011) Fuzzy modeling approaches for the prediction of maximum charge per delay in surface mining. Int J Rock Mech Min Sci 48(2):305–310

    Article  Google Scholar 

  93. Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. JES J Eng Sci 39(2):425–440

    Google Scholar 

  94. Wang X, Liu X, Pedrycz W, Zhu X, Hu G (2012) Mining axiomatic fuzzy set association rules for classification problems. Eur J Oper Res 218(1):202–210

    Article  MathSciNet  Google Scholar 

  95. Gajewski J, Jedliński Ł, Jonak J (2013) Classification of wear level of mining tools with the use of fuzzy neural network. Tunn Undergr Space Technol 35:30–36

    Article  Google Scholar 

  96. Ghasemi E, Ataei M (2013) Application of fuzzy logic for predicting roof fall rate in coal mines. Neural Comput Appl 22(1):311–321

    Article  Google Scholar 

  97. Wang Y, Yang W, Li M, Liu X (2012) Risk assessment of floor water inrush in coal mines based on secondary fuzzy comprehensive evaluation. Int J Rock Mech Min Sci 52:50–55

    Article  Google Scholar 

  98. Petrović DV, Tanasijević M, Milić V, Lilić N, Stojadinović S, Svrkota I (2014) Risk assessment model of mining equipment failure based on fuzzy logic. Expert Syst Appl 41(18):8157–8164

    Article  Google Scholar 

  99. Song Z, Zhu H, Jia G, He C (2014) Comprehensive evaluation on self-ignition risks of coal stockpiles using fuzzy AHP approaches. J Loss Prev Process Ind 32:78–94

    Article  Google Scholar 

  100. Bahri NA, Ebrahimi FMA, Reza SG (2015) A fuzzy logic model to predict the out-of-seam dilution in longwall mining. Int J Min Sci Technol 25(1):91–98

    Article  Google Scholar 

  101. Jang H, Topal E, Kawamura Y (2015) Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neuro-fuzzy system. Appl Soft Comput 32:1–12

    Article  Google Scholar 

  102. Amani K, Najafi M, Rafiee R (2021) Prediction of coal and gas outburst risk by fuzzy rock engineering system. Environ Earth Sci 80(15):1–15

    Article  Google Scholar 

  103. Habibagahi G, Katebi S (1996) Rock mass classification using fuzzy sets. Iran J Sci Technol Trans B-Eng 20(3):273–284

    Google Scholar 

  104. Namin FS, Rinne M, Rafie M (2015) Uncertainty determination in rock mass classification when using FRMR Software. J S Afr Inst Min Metall 115(11):1073–1082

    Article  Google Scholar 

  105. Sonmez H, Gokceoglu C, Ulusay R (2003) An application of fuzzy sets to the geological strength index (GSI) system used in rock engineering. Eng Appl Artif Intell 16(3):251–269

    Article  Google Scholar 

  106. Aydin A (2004) Fuzzy set approaches to classification of rock masses. Eng Geol 74(3–4):227–245

    Article  Google Scholar 

  107. Khademi Hamidi J, Shahriar K, Rezai B, Bejari H (2010) Application of fuzzy set theory to rock engineering classification systems: an illustration of the rock mass excavability index. Rock Mech Rock Eng 43(3):335–350

    Article  ADS  Google Scholar 

  108. Jalalifar H, Mojedifar S, Sahebi AA, Nezamabadi-Pour H (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38(6):783–790

    Article  Google Scholar 

  109. Jalalifar H, Mojedifar S, Sahebi AA (2014) Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model. Int J Min Sci Technol 24(2):237–244

    Article  Google Scholar 

  110. Mutlu B, Sezer EA, Nefeslioglu HA (2017) A defuzzification-free hierarchical fuzzy system (DF-HFS): rock mass rating prediction. Fuzzy Sets Syst 307:50–66

    Article  MathSciNet  Google Scholar 

  111. Fetz, T (2005) Fuzzy models in geotechnical engineering and construction management. Anal Uncertain Civ Eng 211–239

  112. Provenzano P, Ferlisi S, Musso A (2004) Interpretation of a model footing response through an adaptive neural fuzzy inference system. Comput Geotech 31(3):251–266

    Article  Google Scholar 

  113. Adoko AC, Wu L (2011) Fuzzy inference systems-based approaches in geotechnical engineering: a review. Electron J Geotech Eng 16:1543–1558

    Google Scholar 

  114. Göktepe AB, Lav AH, Altun S, Altıntaş G (2008) Fuzzy decision support system to determine swell/shrink factor affecting earthwork optimization of highways. Math Comput Appl 13(1):61–70

    Google Scholar 

  115. Mikaeil R, Ataei M, Yousefi R (2011) Application of a fuzzy analytical hierarchy process to the prediction of vibration during rock sawing. Min Sci Technol 21(5):611–619

    Google Scholar 

  116. Ahumada A, Altunkaynak A, Ayoub A (2015) Fuzzy logic-based attenuation relationships of strong motion earthquake records. Expert Syst Appl 42(3):1287–1297

    Article  Google Scholar 

  117. Rouhani MM, Namin FS (2023) Investigate the potential of using fuzzy similarity in decision making under uncertainty for mining projects. Resour Policy 86:104087. https://doi.org/10.1016/j.resourpol.2023.104087

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Mining Engineering, Faculty of Engineering, University of Zanjan, Zanjan, 45371-38791, Iran

    Farhad Samimi Namin

  2. Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Matin Rouhani

Authors
  1. Farhad Samimi Namin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Matin Rouhani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FSN was contributed to supervision, project administration, conceptualization, methodology, investigation resources, data validation, Writing—review and editing. MMR was contributed to conceptualization, excel software tool, formal analysis, writing—original draft. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Farhad Samimi Namin.

Ethics declarations

Conflict of interest

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Samimi Namin, F., Rouhani, M.M. A Review: Applications of Fuzzy Theory in Rock Engineering. Indian Geotech J (2024). https://doi.org/10.1007/s40098-024-00910-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40098-024-00910-z

Keywords

Search

Navigation