Skip to main content
SpringerLink
Log in

Geostatistical interpolation for modelling SPT data in northern Izmir

  • Published:
Sadhana Aims and scope Submit manuscript

Abstract

In this study, it was aimed to map the corrected Standard Penetration Test (SPT) values in Karşıyaka city center by kriging approach. Six maps were prepared by this geostatistical approach at depths of 3, 6, 9, 13.5, 18 and 25.5 m. Borehole test results obtained from 388 boreholes in central Karşıyaka were used to model the spatial variation of (N1)60cs values in an area of 5.5 km2. Corrections were made for depth, hammer energy, rod length, sampler, borehole diameter and fines content, to the data in hand. At various depths, prepared variograms and the kriging method were used together to model the variation of corrected SPT data in the region, which enabled the estimation of missing data in the region. The results revealed that the estimation ability of the models were acceptable, which were validated by a number of parameters as well as the comparisons of the actual and estimated data. Outcomes of this study can be used in microzonation studies, site response analyses, calculation of bearing capacity of subsoils in the region and producing a number of parameters which are empirically related to corrected SPT number as well.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10

Similar content being viewed by others

References

  • Al-Suba’i K A and Barat K A 2006 Ground conditions of Sana’a City, Yemen Republic. In: M G Culshaw, H J Reeves, I Jefferson and T W Spink (eds), 10 th Congress of the International Association for Engineering Geology and the Environment – Engineering geology for tomorrow’s cities, Nottingham, Paper No.29, CD-ROM

  • Baecher G B 1984 On estimating auto-covariance of soil properties. In: Y K Wen (ed), Specialty Conference on Probabilistic Mechanics and Structural Reliability, ASCE, 1984, 110, 214–218

  • Baise L G, Higgins R B and Brankman C M 2006 Liquefaction hazard mapping-statistical and spatial characterization of susceptible units. J. Geotech. Geoenviron. Eng. 132(6): 705–715

    Article  Google Scholar 

  • Baker J W and Faber M H 2008 Liquefaction risk assessment using geostatistics to account for soil spatial variability. J. Geotech. Geoenviron. Eng. 134(1): 14–23

    Article  Google Scholar 

  • Basarir H, Kumral M, Karpuz C and Tutluoglu L 2010 Geostatistical modeling of spatial variability of SPT data for a borax stockpile site. Eng. Geol. 114(3–4): 154–163

    Article  Google Scholar 

  • Bowles J E 1997 Foundation analysis and design. Singapore: McGraw-Hill Book Company, 1024 p

  • Budhu M 2007 Soil mechanics and foundations. U.S.A.: Wiley 780 p

    Google Scholar 

  • Carvalho J J C M and Cavalheiro A A T 2005 Geostatistical and fourier analysis applied to cross-hole tomography seismic data. Quant. Geol. Geostat. 14(4): 843–852

    Article  Google Scholar 

  • Çetin K O, Seed R B, Der Kiureghian A, Tokimatsu K, Harder L F Jr, Kayen R E and Moss R E S 2004 Standard penetration test-based probabilistic and deterministic assessment of seismic soil liquefaction potential. J. Geotech. Geoenviron. Eng. 130(12): 1314–1340

    Article  Google Scholar 

  • Çiftçi N B and Bozkurt E 2009 Evolution of the Miocene sedimentary fill of the Gediz Graben, SW Turkey. Sediment. Geol. 216(3–4): 49–79

    Article  Google Scholar 

  • Dawson K M and Baise L G 2005 Three-dimensional liquefaction potential analysis using geostatistical interpolation. Soil Dyn. Earthq. Eng. 25(5): 369–381

    Article  Google Scholar 

  • Duval R, L’evy R and Matheron G 1955 Travaux de D. G. Krige sur l’evaluation des gisements dans les mines d’or sud-africaines. Ann. Min. 12: 3–49

    Google Scholar 

  • Emre Ö, Özalp S, Doǧan A, Özaksoy V, Yıldırım C and Göktaş F 2005 Active faults in the vicinity of İzmir and their earthquake potential. Report No:10754, Turkish General Directorate of Mineral Research and Exploration

  • Huijbregts C J 1975 Regionalized variables and quantitative analysis of spatial data. Display and analysis of spatial data. In: J C Davis, M J Mc Cullagh (eds), New York: John Wiley, 378 p

  • Jaksa M B 1995 The influence of spatial variability on the geotechnical design properties of a stiff, overconsolidated clay. Ph.D. thesis, Faculty of Engineering, The university of Adelaide, Australia, 451 p

  • Journel A 1989 Fundamentals of geostatistics in five lessons. Short course in geology. Washington, D.C.: American Geophysical Union, 134 p

  • Koca Y and Kıncal C 2004 Abandoned stone quarries in and around the Izmir city centre and their geo-environmental impacts-Turkey. Eng. Geol. 75(1): 49–67

    Article  Google Scholar 

  • Krige D G 1951 A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem. Metall. Min. Soc. S. Afr. 52(6): 119–139

    Google Scholar 

  • Lenz J A and Baise L G 2007 Spatial variability of liquefaction potential in a region mapping using CPT and SPT data. Soil Dyn. Earthq. Eng. 27(7): 690–702

    Article  Google Scholar 

  • Mansor S S B 2003 Analysis of site classification for Kuala Lumpur’s ground assessment. M.Sc. Thesis, Civil Engineering Faculty, Universiti Sains Malaysia, 174 p

  • Matheron G 1970 The theory of regionalized variables and its applications. No.5 in Les Cahiers du Centre de Morphologie Mathématique. Ecole de Mines de Paris, Fontainebleau, 207 p

  • Mendes R M and Lorandi R 2003 Study of SPT behavior of penetration resistance under influence of the groundwater level fluctuation, using geostatistical methodology. RMZ-Mater. Geoenviron. 50(1): 225–228

    Google Scholar 

  • Mendes R M and Lorandi R 2008 Analysis of spatial variability of SPT penetration resistance in collapsible soils considering water table depth. Eng. Geol. 101(3–4): 218–225

    Article  Google Scholar 

  • Olea R A (ed) 1991 Geostatistical glossary and multilingual dictionary. USA: Oxford University Press, 192 p

    Google Scholar 

  • Önalp A and Sert S 2010 Geotechnical information III: foundations. İstanbul: Birsen Publisher, 426 p (in Turkish)

    Google Scholar 

  • Parsons R L and Frost J D 2002 Evaluating site investigation using GIS and geostatistics. J. Geotech. Geoenviron. Eng. 128(6): 451–461

    Article  Google Scholar 

  • Rota 2007 Estimating uncertainty in 3D Modeling using statistics. Ph.D. Thesis, Universita degli Studi di Pavia, 116 p

  • Samui P and Sitharam T G 2010 Site characterization model using artificial neural network and kriging: Int. J. Geomech. 10(5): 171–180

    Article  Google Scholar 

  • Sitharam T G and Samui P 2007 Geostatistical modelling of spatial and depth variability of SPT data for Bangalore. Geomech. GeoEng. 2(4): 307–316

    Article  Google Scholar 

  • Sivrikaya O and Togrol E 2006 Determination of undrained strength of fine-grained soils by means of SPT and its application in Turkey. Eng. Geol. 86(1): 52–69

    Article  Google Scholar 

  • Whitman R V 2000 Organizing and evaluating uncertainty in geotechnical engineering. J. Geotech. Geoenviron. Eng. 126(7): 583–593

    Article  Google Scholar 

Download references

Acknowledgement

This study is based on the data obtained from projects 105M336 and 106M013, supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK).

Author information

Authors and Affiliations

  1. Civil Engineering Department, Ege University, 35100, Izmir, Turkey

    SELIM ALTUN & ALPER SEZER

  2. Rruga Bilal Golemi, Tirana, Albania

    A BURAK GÖKTEPE

Authors
  1. SELIM ALTUN
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A BURAK GÖKTEPE
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ALPER SEZER
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ALPER SEZER.

List of symbols

List of symbols

a :

Range in model

C :

Variance between predetermined two points

C 0 :

Nugget in model

C B :

Borehole diameter correction

C E :

Hammer energy correction

C fines :

Fines content correction

C N :

Overburden pressure correction

C R :

Rod length correction

C S :

Sampler correction

CPT:

Cone penetration test

CSR:

Cyclic stress ratio

FC :

Percentage of fines content

GIS :

Geographic information system

H :

Lag distance between two points

LPI:

Liquefaction probability index

MAE:

Mean absolute error

(N1)60 :

Corrected number of blow counts

(N1)60cs :

Number of blow counts corrected by fines content

N(h):

Number of data pairs separated within a predefined lag distance

p:

Estimated value

R:

Refusal in standard penetration test

RMSE:

Root mean square error

SPT:

Standard penetration test

SPT-N:

Experimentally obtained standard penetration value

UTM:

Universal transverse mercator

u :

Location of variable z

y :

Calculated value

z :

Variable in kriging

α :

Lagrange coefficient used in the minimization of the error variance

γ(h):

Variogram

Rights and permissions

Reprints and permissions

About this article

Cite this article

ALTUN, S., GÖKTEPE, A.B. & SEZER, A. Geostatistical interpolation for modelling SPT data in northern Izmir. Sadhana 38, 1451–1468 (2013). https://doi.org/10.1007/s12046-013-0183-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12046-013-0183-8

Keywords

Search

Navigation