Abstract
A large amount of geotechnical investigation data is essential for the highly reliable design of geotechnical structure at a construction site. The number of geotechnical investigations, however, has been generally insufficient and spatially biased owing to economic and spatial-temporal limitations. In this study, a geotechnical three-dimensional spatial modeling was implemented using an optimized geostatistical interpolation approach at a bridge construction site in the south-central part of the Korean peninsula. The geotechnical investigation data were collected and standardized for the construction of a geo-database. For the site-specific stratification, a kriging-based integration of the geo-layers and the seismic velocity from a seismic refraction survey were applied. The value from a standard penetration test (SPT)-N of an uninvestigated location was predicted using parametric and nonparametric geostatistical methods. We accomplished three-dimensional spatial interpolations using ordinary kriging, a sequential Gaussian simulation with a normal score transformed dataset, and a sequential indicator simulation using the geodatabase. A leave-one-out cross validation was carried out to quantitatively evaluate the reliability of the three-dimensional modeling. Finally, a three-dimensional geotechnical spatial model assigned with subsurface stratification and SPT-N values was constructed using the sequential Gaussian simulation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdideh M, Ghasemi A (2014) A comparison of various statistical and geostatistical methods in estimating the geomechanical properties of reservoir rocks. Petroleum Science and Technology 32(9):1058–1064, DOI: https://doi.org/10.1080/10916466.2011.639321
Aghamolaie I, Lashkaripour GR, Ghafoori M, Moghaddas NH (2018) 3D geotechnical modeling of subsurface soils in Kerman city, southeast Iran. Bulletin of Engineering Geology and the Environment 78(3):1385–1400, DOI: https://doi.org/10.1007/s10064-018-1240-7
Altun S, Göktepe AB, Sezer A (2013) Geostatistical interpolation for modelling SPT data in northern Izmir. Sadhana 38(6):1451–1468, DOI: https://doi.org/10.1007/s12046-013-0183-8
Anbazhagan P, Parihar A, Rashmi HN (2012) Review of correlations between SPT N and shear modulus: A new correlation applicable to any region. Soil Dynamics and Earthquake Engineering 36:52–69, DOI: https://doi.org/10.1016/j.soildyn.2012.01.005
Basarir H, Kumral M, Karpuz C, Tutluoglu L (2010) Geostatistical modeling of spatial variability of SPT data for a borax stockpile site. Engineering Geology 114(3–4):154–163, DOI: https://doi.org/10.1016/j.enggeo.2010.04.012
Daniel CR (2000) Split spoon penetration testing in gravels. MSc Thesis, The University of British Columbia, Vancouver, Canada
Dimitrakopoulos R (1998) Conditional simulation algorithms for modelling orebody uncertainty in open pit optimization. International Journal of Surface Mining, Reclamation and Environment 12(4):173–179, DOI: https://doi.org/10.1080/09208118908944041
Dong M, Neukum C, Hu H, Azzam R (2014) Real 3D geotechnical modeling in engineering geology: A case study from the inner city of Aachen, Germany. Bulletin of Engineering Geology and the Environment 74(2):281–300, DOI: https://doi.org/10.1007/s10064-014-0640-6
Dung NT, Chung SG, Kim SR, Beak SH (2011) Applicability of the SPT-based methods for estimating toe bearing capacity of driven PHC piles in the thick deltaic deposits. KSCE Journal of Civil Engineering 15(6):1023–1031, DOI: https://doi.org/10.1007/s12205-011-0801-0
Dungan JL (1999) Conditional simulation: An alternative to estimation for achieving mapping objectives. In: Stein A, Van der F, Gorte B (eds) Spatial statistics for remote sensing, remote sensing and digital image processing, vol. 1. Springer Netherlands, Dordrecht, Netherlands, 135–152
Elumalai V, Brindha K, Sithole B, Lakshmanan E (2017) Spatial interpolation methods and geostatistics for mapping groundwater contamination in a coastal area. Environmental Science and Pollution Research 24(12):11601–11617, DOI: https://doi.org/10.1007/s11356-017-8681-6
Emery X (2004) Properties and limitations of sequential indicator simulation. Stochastic Environmental Research and Risk Assessment 18(6):414–424, DOI: https://doi.org/10.1007/s00477-004-0213-5
Esfahani NM, Asghari O (2012) Fault detection in 3D by sequential Gaussian simulation of Rock Quality Designation (RQD). Arabian Journal of Geosciences 6(10):3737–3747, DOI: https://doi.org/10.1007/s12517-012-0633-3
Gomez-Hernandez JJ, Srivastava RM (1990) ISIM3D: An ANSI-C three-dimensional multiple indicator conditional simulation program. Computers and Geosciences 16(4):395–440, DOI: https://doi.org/10.1016/0098-3004(90)90010-Q
Goovaerts P (1994) Comparative performance of indicator algorithms for modeling conditional probability distribution functions. Mathematical Geology 26(3):389–411, DOI: https://doi.org/10.1007/BF02089230
Goovaerts P (2001) Geostatistical modelling of uncertainty in soil science. Geoderma 103(1–2):3–26, DOI: https://doi.org/10.1016/S0016-7061(01)00067-2
Goovaerts P (2009) AUTO-IK: A 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences. Computers & Geosciences 35(6):1255–1270, DOI: https://doi.org/10.1016/j.cageo.2008.08.014
Gringarten E, Deutsch CV (1999) Methodology for variogram interpretation and modeling for improved reservoir characterization. SPE annual technical conference and exhibition, October 3–6, Houston, TX, USA
Gringarten E, Deutsch CV (2001) Teacher’s aide variogram interpretation and modeling. Mathematical Geology 33(4):507–534, DOI: https://doi.org/10.1023/A:1011093014141
Hosseini E, Gholami R, Hajivand F (2018) Geostatistical modeling and spatial distribution analysis of porosity and permeability in the Shurijeh-B reservoir of Khangiran gas field in Iran. Journal of Petroleum Exploration and Production Technology 9(2):1051–1073, DOI: https://doi.org/10.1007/s13202-018-0587-4
Huo XN, Li H, Sun DF, Zhou LD, Li BG (2012) Combining geostatistics with Moran’s I analysis for mapping soil heavy metals in Beijing, China. International Journal of Environmental Research and Public Health 9(3):995–1017, DOI: https://doi.org/10.3390/ijerph9030995
Kim HS, Chung CK, Kim HK (2016) Geo-spatial data integration for subsurface stratification of dam site with outlier analyses. Environmental Earth Sciences 75:168, DOI: https://doi.org/10.1007/s12665-015-4931-4
Kim HS, Kim HK, Shin SY, Chung CK (2012a) Application of statistical geo-spatial information technology to soil stratification in the Seoul metropolitan area. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 6(4):221–228, DOI: https://doi.org/10.1080/17499518.2012.744248
Kim HR, Kim KH, Yun ST, Hwang SI, Kim HD, Lee GT, Kim YJ (2012b) Evaluation of geostatistical approaches for better estimation of polluted soil volume with uncertainty evaluation. Journal of Soil and Groundwater Environment 17(6):69–81, DOI: https://doi.org/10.7857/JSGE.2012.17.6.069
Kovacs WD, Salomone LA (1982) SPT hammer energy measurement. Journal of the Geotechnical Engineering Division 108(4):599–620
Krige DG, Magri EJ (1982) Studies of the effects of outliers and data transformation on variogram estimates for a base metal and a gold ore body. Mathematical Geology 14(6):557–564, DOI: https://doi.org/10.1007/BF01033879
Leung YF, Liu W, Lei Y, Hsu SC (2018) Quantifying cost-effectiveness of subsurface strata exploration in excavation projects through geostatistics and spatial tessellation. Automation in Construction 90:243–252, DOI: https://doi.org/10.1016/j.autcon.2018.02.032
Liao SSC, Whitman RV (1986) Overburden correction factors for SPT in sand. Journal of Geotechnical Engineering 112(3):373–377, DOI: https://doi.org/10.1061/(ASCE)0733-9410(1986)112:3(373)
Lombardo L, Bakka H, Tanyas H, Westen C, Mai PM, Huser R (2018) Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides. JGR Earth Surface 124(7):1958–1980, DOI: https://doi.org/10.1029/2019JF005056
Marache A, Breysse D, Piette C, Thierry P (2009) Geotechnical modeling at the city scale using statistical and geostatistical tools: The Pessac case (France). Engineering Geology 107(3–4):67–76, DOI: https://doi.org/10.1016/j.enggeo.2009.04.003
Mujtaba H, Farooq K, Sivakugan N, Das BM (2017). Evaluation of relative density and friction angle based on SPT-N values. KSCE Journal of Civil Engineering 22(2):572–581, DOI: https://doi.org/10.1007/s12205-017-1899-5
Mundepi AK, Galiana-Merino JJ, Kamal, Lindholm C (2010) Soil characteristics and site effect assessment in the city of Delhi (India) using H/V and f-k methods. Soil Dynamics and Earthquake Engineering 30(7):591–599, DOI: https://doi.org/10.1016/j.soildyn.2010.01.016
Oh S, Chung H, Lee DK (2004) Geostatistical integration of MT and borehole data for RMR evaluation. Environmental Geology 46:1070–1078, DOI: https://doi.org/10.1007/s00254-004-1115-z
Oh S, Sun CG (2007) Combined analysis of electrical resistivity and geotechnical SPT blow counts for the safety assessment of fill dam. Environmental Geology 54(1):31–42, DOI: https://doi.org/10.1007/s00254-007-0790-y
Ozturk H, Erkayaoglu M (2018) Interpretation of variability of rock mass rating by geostatistical analysis: A case study in Western Turkey. Arabian Journal of Geosciences 11(13):344, DOI: https://doi.org/10.1007/s12517-018-3696-y
Pinheiro M, Emery X, Miranda T, Lamas L, Espada M (2018) Modelling geotechnical heterogeneities using geostatistical simulation and finite differences analysis. Minerals 8(2):52, DOI: https://doi.org/10.3390/min8020052
Qi XH, Liu HX (2019) An improved global zonation method for geotechnical parameters. Engineering Geology 248:185–196, DOI: https://doi.org/10.1016/j.enggeo.2018.11.013
Rahimi H, Asghari O, Hajizadeh F (2018) Selection of optimal thresholds for estimation and simulation based on indicator values of highly skewed distributions of ore data. Natural Resources Research 27(4):437–453, DOI: https://doi.org/10.1007/s11053-017-9366-z
Samui P, Sitharam TG (2010) Site characterization model using artificial neural network and kriging. International Journal of Geomechanics 10(5):171–180, DOI: https://doi.org/10.1061/(ASCE)1532-3641(2010)10:5(171)
Schröder W (2006) GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology. International Journal of Medical Microbiology 296(40):23–36, DOI: https://doi.org/10.1016/j.ijmm.2006.02.015
Sideri D, Modis K, Rozos D (2014) Multivariate geostatistical modelling of geotechnical characteristics of the alluvial deposits in West Thessaly, Greece. Bulletin of Engineering Geology and the Environment 73(3): 709–722, DOI: https://doi.org/10.1007/s10064-013-0568-2
Sun CG, Kim HS (2016) Geostatistical assessment for the regional zonation of seismic site effects in a coastal urban area using a GIS framework. Bulletin of Earthquake Engineering 14(8):2161–2183, DOI: https://doi.org/10.1007/s10518-016-9908-5
Thoang TT, Giao PH (2015) Subsurface characterization and prediction of land subsidence for HCM City, Vietnam. Engineering Geology 199(107–124), DOI: https://doi.org/10.1016/j.enggeo.2015.10.009
Veronesi F, Corstanje R, Mayr T (2012) Mapping soil compaction in 3D with depth functions. Soil and Tillage Research 124:111–118, DOI: https://doi.org/10.1016/j.still.2012.05.009
Warrick AW, Myers DE, Nielsen DR (1986) Geostatistical methods applied to soil science. In: Methods of soil analysis: Part 1 — Physical and mineralogical methods, 2nd edition. Soil Science Society of America, American Society of Agronomy, Madison, WI, USA, 53–82, DOI: https://doi.org/10.2136/sssabookser5.1.2ed.c3
Wu J, Norvell WA, Welch RM (2006) Kriging on highly skewed data for DTPA-extractable soil Zn with auxiliary information for pH and organic carbon. Geoderma 134(1–2):187–199, DOI: https://doi.org/10.1016/j.geoderma.2005.11.002
Yamamoto JK (2000) An alternative measure of the reliability of ordinary kriging estimates. Mathematical Geology 32(4):489–509, DOI: https://doi.org/10.1023/A:1007577916868
Yamamoto JK (2005) Correcting the smoothing effect of ordinary kriging estimates. Mathematical Geology 37(1):69–94, DOI: https://doi.org/10.1007/s11004-005-8748-7
Zarco-Perello S, Simões N (2017) Ordinary kriging vs inverse distance weighting: Spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico. Peer J 30(5):e4078, DOI: https://doi.org/10.7717/peerj.4078
Zhang C, Li W (2008) Regional-scale modelling of the spatial distribution of surface and subsurface textural classes in alluvial soils using Markov chain geostatistics. Soil Use and Management 24(3):263–272, DOI: https://doi.org/10.1111/j.1475-2743.2008.00165.x
Acknowledgements
This research was supported by a grant(19SCIP-B119960-04) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government and supported by Institute of Construction and Environmental Engineering at Seoul National University.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, M., Kim, HS. & Chung, CK. A Three-Dimensional Geotechnical Spatial Modeling Method for Borehole Dataset Using Optimization of Geostatistical Approaches. KSCE J Civ Eng 24, 778–793 (2020). https://doi.org/10.1007/s12205-020-1379-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-020-1379-1