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Geostatistical modeling of uniaxial compressive strength along the axis of the Behesht-Abad tunnel in Central Iran

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Abstract

Uniaxial compressive strength (UCS) is one of the most significant factors in the stability of rock engineering projects and is vastly used during the design and construction stages of underground excavations. Distances between exploratory boreholes is typically large during the preliminary studies of a majority of geotechnical projects. As a result, estimation of UCS values based on the scant data obtained from these distant boreholes is crucially important. Thus, the use of geostatistical estimation and simulation methods can lead to a significant reduction of errors and exploratory costs. In this regard, the present paper ties simulation and estimation of UCS values of the rock material between boreholes of the Behesht-Abad tunnel in central Iran using the Stanford Geostatistical Modeling Software (SGEMS). Variography showed the acceptable spatial distribution (9,477 m) of UCS parameters in the area under investigation. Also, cross-validation proved the high accuracy (98 %) and reliability of the results of the developed model. Then, considering the condition of the datasets and engineering judgmentvalues, the model outcomes were analyzed. It was concluded there is high similarity between the geostatistical estimation/simulation and engineering judgments. Application of geostatistical simulation methods in conjunction with estimation in rock research indicates these methods are performed in a rather different aspect from those of common validation processes. Furthermore, the combination of geostatistical estimation/simulation and engineering judgment led to better identification of the pros and cons of geotechnical explorations in each tunnel route.

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References

  • Armstrong M (1998) Basic linear geostatistics. Springer, Berlin

    Book  Google Scholar 

  • Ayalew L, Reik G, Busch W (2002) Characterizing weathered rock masses—a geostatistical approach. Int J Rock Mech Min Sci 39(1):105–114

    Article  Google Scholar 

  • Brook N (1985) The equivalent core diameter method of size and shape correction in point load testing. Int J Rock Mech Mining Sci Geomech Abstr. Vol. 22. No. 2. Pergamon

  • Coates DF, Parsons RC (1966) Experimental criteria for classification of rock substances. Int J Rock Mech Mining Sci Geomech Abstr. Vol. 3, No. 3, pp 181–189. Pergamon

  • Costa JF, Zingano AC, Koppe JC (2000) Simulation—an approach to risk analysis in coal mining. Explor Min Geol 9(1):43–49

    Article  Google Scholar 

  • Del Potro R, Hürlimann M (2009) A comparison of different indirect techniques to evaluate volcanic intact rock strength. Rock Mech Rock Eng 42(6):931–938

    Article  Google Scholar 

  • Diamantis K, Gartzos E, Migiros G (2009) Study on uniaxial compressive strength, point load strength index, dynamic and physical properties of serpentinites from Central Greece: test results and empirical relations. Eng Geol 108(3):199–207

    Article  Google Scholar 

  • Dubrule O (2003) Geostatistics for seismic data integration in earth models: 2003 Distinguished Instructor Short Course. No. 6.SEG Books

  • Ellefmo SL, Eidsvik J (2009) Local and spatial joint frequency uncertainty and its application to rock mass characterisation. Rock Mech Rock Eng 42(4):667–688

    Article  Google Scholar 

  • Esfahani NM, Asghari O (2013) Fault detection in 3D by sequential Gaussian simulation of rock quality designation (RQD). Arab J Geosci 6(10):3737–3747

    Article  Google Scholar 

  • Gill DE, Corthésy R, Leite MH (2005) Determining the minimal number of specimens for laboratory testing of rock properties. Eng Geol 78(1):29–51

    Article  Google Scholar 

  • Gringarten E, Deutsch CV (2001) Teacher’s aide variogram interpretation and modeling. Math Geol 33(4):507–534

    Article  Google Scholar 

  • Hashemi M (2008) Rock mechanic’s reports, water supply project of the Central Plateau, Zayandehab Consulting, printed in farsi

  • Hoerger SF, Young DS (1987) Predicting local rock mass behavior using geostatistics. The 28th US Symposium on Rock Mechanics (USRMS)

  • Journel AG (1988) Fundamentals of geostatistics in five lessons, vol 8. American Geophysical Union, Washington, DC

    Google Scholar 

  • Krige DG (1951) A statistical approach to some basic mine valuation problems on theWitwatersrand. J Chem Metall Mining Soc S Afr 52:119–139

    Google Scholar 

  • La Pointe PR (1980) Analysis of the spatial variation in rock mass properties through geostatistics. The 21st US Symposium on Rock Mechanics (USRMS)

  • Manouchehrian A, Sharifzadeh M, Hamidzadeh MR (2012) Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. Int J Mining Sci Technol 22(2):229–236

    Article  Google Scholar 

  • Mark C, Williams LM, Pappas D, Rusnak J (2004) Spatial trends in rock strength: can they be determined from coreholes. In: Proceedings of the 23rd International Conference on Ground Control in Mining. Morgantown, WV: West Virginia University, pp 177B182

  • 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 

  • Monjezi M, Amini KH, Razifard M (2012) A neuro-genetic network for predicting uniaxial compressive strength of rocks. Geotech Geol Eng 30(4):1053–1062

    Article  Google Scholar 

  • Oh S (2013) Geostatistical integration of seismic velocity and resistivity data for probabilistic evaluation of rock quality. Environ Earth Sci 69:939–945

    Article  Google Scholar 

  • Ozbek A, Unsal M, Dikec A (2013) Estimating uniaxial compressive strength of rocks using genetic expression programming. J Rock Mech Geotech Eng 5(4):325–329

    Article  Google Scholar 

  • Ozturk CA, Nasuf E (2002) Geostatistical assessment of rock zones for tunneling. Tunn Undergr Space Technol 17(3):275–285

    Article  Google Scholar 

  • Ozturk CA, Simdi E (2014) Geostatistical investigation of geotechnical and constructional properties in Kadikoy-Kartal subway, Turkey. Tunn Undergr Space Technol 41:35–45

    Article  Google Scholar 

  • Rajashekhar MR, Ellingwood BR (1993) A new look at the response surface approach for reliability analysis. Struct Saf 12(3):205–220

    Article  Google Scholar 

  • Remy N, Boucher A, Wu J (2009) Applied geostatistics with SGeMS: a user’s guide. Cambridge University, Cambridge

  • Rosenbaum MS, Rosén L, Gustafson G (1997) Probabilistic models for estimating lithology. Eng Geol 47(1):43–55

    Article  Google Scholar 

  • Ruffolo RM, Shakoor A (2009) Variability of unconfined compressive strength in relation to number of test samples. Eng Geol 108(1):16–23

    Article  Google Scholar 

  • Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4(4):409–423

    Article  Google Scholar 

  • Stavropoulou M, Exadaktylos G, Saratsis G (2007) A combined three-dimensional geological–geostatistical–numerical model of underground excavations in rock. Rock Mech Rock Eng 40(3): 213–243

  • Stright L, Bernhardt A, Boucher A (2013) DFTopoSim: modeling topographically-controlled deposition of subseismic scale sandstone packages within a mass transport dominated deep-water channel belt. Math Geosci 45(3):277–296

    Article  Google Scholar 

  • Vann J, Bertoli O, Jackson S (2002) An overview of geostatistical simulation for quantifying risk. Geostatistical Association of Australian symposium 1:1

    Google Scholar 

  • Yilmaz I (2009) A new testing method for indirect determination of the unconfined compressive strength of rocks. Int J Rock Mech Min Sci 46(8):1349–1357

    Article  Google Scholar 

  • Yilmaz I, Sendir H (2002) Correlation of Schmidt hardness with unconfined compressive strength and Young’s modulus in gypsum from Sivas (Turkey). Eng Geol 66(3):211–219

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the Zayandehab Consulting group for providing quality laboratory and field data for this research.

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Authors and Affiliations

  1. School of Mining Engineering, University College of Engineering, University of Tehran, Tehran, Iran

    M. Doostmohammadi & A. Jafari

  2. Simulation and Data Processing Laboratory, School of Mining Engineering, University College of Engineering, University of Tehran, Tehran, Iran

    O. Asghari

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  1. M. Doostmohammadi
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  2. A. Jafari
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  3. O. Asghari
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Correspondence to O. Asghari.

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Doostmohammadi, M., Jafari, A. & Asghari, O. Geostatistical modeling of uniaxial compressive strength along the axis of the Behesht-Abad tunnel in Central Iran. Bull Eng Geol Environ 74, 789–802 (2015). https://doi.org/10.1007/s10064-014-0663-z

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  • DOI: https://doi.org/10.1007/s10064-014-0663-z

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