Classification of Limestone Rock Masses Using Laboratory and Field P-wave Velocity by ArcGIS Fuzzy Overlay (AFO) (Case Study: Five Dam Sites in Zagros Mountains, Western Iran)

  • Mehdi Kianpour
  • Seyed Mahmoud Fatemi AghdaEmail author
  • Mehdi Talkhablou
Original Paper


In this study, 18 seismic tomography profiles in limestone rock masses with a total length of 2450 meters were studied to find the relationship between P-wave velocity in field and laboratory (VPField and VPLab) and rock mass classifications (Q and Qsrm) in the Zagros mountains, western Iran. The results showed that the Q rock mass classification system and its modified system for sedimentary rock masses (Qsrm) were closely related to Vp parameters. In addition to the VPField, KP (VPField/VPLab) also showed a significant relationship with Q and Qsrm. The best multivariate equation between VPField and KP with Qsrm (R2 = 0.70) was more reliable and had less error than the best between VPField and KP with Q (R2 = 0.55). The reason for this was the status of the voids and some other rock mass properties such as bedding and structure of rock mass in calculating the Qsrm, which were neglected in calculating the Q index. Certainly, voids and many rock mass properties in the limestone were very effective in the amount of KP and VPField. Also, the development of models of ArcGIS fuzzy overlay (AFO) methods for prediction of Qsrm showed very interesting results because it was able to show stronger accuracy (R2 = 0.89). In order to check the accuracy and errors of the obtained models, “Error%”, “Mean Absolute Percentage Error”, “Mean Absolute Deviation” and “Root Mean Square Error” indicators were used which showed the high-efficiency of the AFO model in estimation the Qsrm. According to the results of this study, it was proposed: in the studied calcareous rock masses, Qsrm classification can be a suitable substitute for Q classification.


Limestone rock mass Field P-wave velocity Laboratory P-wave velocity Multivariate regression analyses ArcGIS fuzzy overlay Predictive models 



  1. Alavi M (2004) Regional stratigraphy of the Zagros fold-thrust belt of Iran and its preforland evaluation. Am J Sci 304:1–20CrossRefGoogle Scholar
  2. Alvarez Grima M, Babuska R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36:339–349CrossRefGoogle Scholar
  3. Andy A, Bery Rosli S (2012) Correlation of seismic P-wave velocities with engineering parameters (N value and rock quality) for tropical environmental study. Int J Geosci 3:749–757CrossRefGoogle Scholar
  4. Anon (1979) Classification of rocks and soils for engineering geological mapping. Bull Int Assoc Eng Geol 5:89–112Google Scholar
  5. Barton N (1991) Geotechnical design. World Tunnelling, pp 410–416Google Scholar
  6. Barton N (1995) The influence of joint properties in modelling jointed rock masses. In: Fujii T (ed) Proc. Int. ISRM Congr. on Rock Mech, Tokyo, Japan. A.A. Balkema, Rotterdam, pp 1023–1032Google Scholar
  7. Barton N (2002) Some new Q-value correlations to assist in site characterization and tunnel design. Int J Rock Mech Min Sci 39:185–216CrossRefGoogle Scholar
  8. Barton N (2007a) Rock quality, seismic velocity, attenuation, and anisotropy. Taylor & Francis Group, London, pp 19–48Google Scholar
  9. Barton N (2007) Future directions for rock mass classification and characterization—Towards a cross-disciplinary approach, Oslo, NorwayGoogle Scholar
  10. Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. J Rock Mech Eng 6(4):189–236CrossRefGoogle Scholar
  11. Benedikt J (2007) The Role of new technologies in environmental monitoring, fuzzy logic in GIS and remote sensing. Geologic 6:18–36Google Scholar
  12. Bery A, Saad R (2012) Correlation of seismic P-wave velocities with engineering parameters (N value and rock quality) for tropical environmental study. Int J Geosci 3(4):749–757CrossRefGoogle Scholar
  13. Bonham GF (1994) Geographic information systems for geoscientists: modelling with GIS, Chapter 9, Fuzzy logic section with related tables and figures. Pergamon, New York, p 398Google Scholar
  14. Cao A, Dou L, Cai W, Gong S, Liu S, Jing G (2015) Case study of seismic hazard assessment in underground coal mining using passive tomography. Int J Rock Mech Min Sci 78:1–9CrossRefGoogle Scholar
  15. Carrozzo MT, Leucci T, Margiotta S, Mazzone F, Negri S (2008) Integrated geophysical and geological investigations applied to sedimentary rock mass characterization. Ann Geophys 51(1):191–202Google Scholar
  16. Cha YH, Kang JS, Jo CH (2006) Application of linear-array microtremor surveys for rock mass classification in urban tunnel design. Explor Geophys 37(1):108–113CrossRefGoogle Scholar
  17. Chrisman N (1997) Exploring geographic information systems. John Wiley and Sons, Hoboken, p 250Google Scholar
  18. Darvishzadeh A (1991) Geology of Iran. Neda Publication, Tehran, pp 1–901Google Scholar
  19. Deere DU (1967) Technical description of rock cores for engineering purposes. J Rock Mech Eng Geol 1:16–22Google Scholar
  20. Fan LF, Wang LJ, Wu ZJ (2018) Wave transmission across linearly jointed complex rock masses. Int J Rock Mech Min Sci 112:193–200CrossRefGoogle Scholar
  21. Fildes R, Goodwin P (2007) against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces 37:570–576CrossRefGoogle Scholar
  22. Gokceoglu C, Yesilnacar E, Sonmez H, Kayabasi A (2004) A neuro-fuzzy model for modulus of deformation of jointed rock masses. J Comput Geotech 31(5):375–383CrossRefGoogle Scholar
  23. Hemmati Nourani M, Taheri Moghadder M, Safari M (2017) Classification and assessment of rock mass parameters in Choghart iron mine using P-wave velocity. J Rock Mech Geotech Eng 9(2):318–328CrossRefGoogle Scholar
  24. Iran Water and Power Resourced Development Co (2012) Final reports of rock geotechnical study of Dams in Zagros regtion, Tehran, IranGoogle Scholar
  25. Karimi H, Tavakkoli M (2007) Assessment of the appeared water origin in the water tunnel of power house of Seymarehdam, Ilam. J Eng Geol 2(1):23–30Google Scholar
  26. Karkazi A, Hatzichristos T, Mavropoulos A, Emmanouilidou B, Elseoud A (2001) Landfill siting using GIS and fuzzy Logic. EPEM S.A. Department of holid and hazardous wastes, Greece; Egyptian Environmental Affairs Agency; and Dept. of geography, National technical university of Athens, GreeceGoogle Scholar
  27. Koleini M (2012) Engineering geological assessment and rock mass characterization of the Asmari formation (Zagros range) as large dam foundation rocks in southwestern Iran. PhD. thesis, University of Pretoria, South AfricaGoogle Scholar
  28. Krau F, Giese R, Alexandrakis C, Buske S (2014) Seismic travel-time and attenuation tomography to characterize the excavation damaged zone and the surrounding rock mass of a newly excavated ramp and chamber. Int J Rock Mech Min Sci 70:524–532CrossRefGoogle Scholar
  29. Larson VE, Golaz J, Cotton W (2002) Small-scale and mesoscale variability in cloudy boundary layers: joint probability density functions. J Atmos Sci 59:3519–3539CrossRefGoogle Scholar
  30. Leucci G, Giorgi LD (2015) 2D and 3D seismic measurements to evaluate the collapse risk of an important prehistoric cave in soft carbonate rock. Open Geosci 7:84–94Google Scholar
  31. Lewellen WS, Yoh S (1993) Binormal model of ensemble partial cloudiness. J Atmos Sci 50:1228–1237CrossRefGoogle Scholar
  32. McDowell PW (1993) Seismic investigation for rock engineering. In: Hudson JA (ed) Comprehensive Rock Engineering, vol 3. Rock Testing and Site Characterization. Pergamon Press, Oxford, pp 619–634Google Scholar
  33. Ross TJ (1995) Fuzzy logic with engineering applications. McGraw-Hill, New York, p 600Google Scholar
  34. Zadeh LA (1984) Making computer think like people. IEEE Spectr 8:26–32CrossRefGoogle Scholar
  35. Zafirovski Z, Peševsk I, Papić J (2012) Methodology for extrapolation of rock mass deformability parameters in tunneling FACTA UNIVERSITATIS Series: architecture and Civil. Engineering 10(3):235–244Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehdi Kianpour
    • 1
  • Seyed Mahmoud Fatemi Aghda
    • 1
    Email author
  • Mehdi Talkhablou
    • 1
  1. 1.Department of Applied Geology, Faculty of Earth ScienceKharazmi UniversityTehranIran

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