Estimating soil resistance at unsampled locations based on limited CPT data
- 182 Downloads
An assessment of soil properties at unsampled locations is a common requirement during the design of a geotechnical structure/system. Such an assessment is challenging due to inherent soil variability and the limited number of in situ tests currently available. Here we present a three-dimensional conditioned random field approach for the estimation of anisotropic soil resistance at unsampled locations based only on data from a limited number of cone penetration tests (CPT). The novelty of this work is that the measured CPT data at arbitrary locations can be combined with random field theory to update the estimated soil properties in three dimensions. The accuracy of the estimation can be improved significantly using the proposed method. A case study in Australia is conducted to illustrate the procedure and to assess the capability of the method. The results indicate that the mean values of the conditioned random fields are good estimates of the soil resistance at unsampled locations. The prediction error of the normalized cone penetration resistance is about 0.08 when there are only six CPT tests. A unique feature of this method is its ability to obtain high-resolution results of soil properties in three-dimensional space using a limited number of CPT tests.
KeywordsSite investigation Random field Three dimensions High-dimensional posterior distribution Uncertainty
The authors would like to acknowledge the support of the Natural Science Foundation of China (Grant No. 51679060). This study represents part of the activities of the Centre for Offshore Foundation Systems of the University of Western Australia.
- Baecher GB, Christian JT (2003) Reliability and statistics in geotechnical engineering. Wiley, New YorkGoogle Scholar
- Ching J, Sung SP (2016) Simulating a curve average in a stationary normal random field using Fourier series method. J GeoEng 11(1):33–43Google Scholar
- Eijnden APVD, Hicks MA (2011) Conditional simulation for characterising the spatial variability of sand state. In: Pietruszczak, Pande GN (eds) Computational Geomechanics, COMGEO II-Proc 2nd Int Symp on Computational Geomechanics. Cavtat-Dubrovnik, Croatia. IC2E International Centre for Computational Engineering, Rhodes, pp 288–296Google Scholar
- Gilbert RB, McGrath TC (1998) Design of site investigation programs for geotechnical engineering. In: Ayyub BM (ed) Chapter 24, uncertainty modeling and analysis in civil engineering. CRC Press, Boca Paton, pp 447–485Google Scholar
- Jiang SH, Papaioannou I, Straub D (2017) Optimizing borehole locations for slope reliability assessment. In: Huang JS, Fenton GA, Zhang LM, Griffiths DV (eds) Geo-Risk 2017: Geotechnical Risk from Theory to Practice. June 4–7, 2017, Denver, Colorado. Geo-Risk 2017. American Society of Civil Engineers, Reston, pp 420–430Google Scholar
- Lacasse S, Nadim F (1996) Uncertainties in characterizing soil properties. In: Shackelford CD, Nelson PP Roth MJS (eds) Uncertainty in the geologic environment: from theory to practice. Proc Uncertainty ‘96. Geotechnical Special Publication 58:49–75Google Scholar
- Li JH, Huang J, Cassidy MJ, Kelly R (2014) Spatial variability of the soil at the Ballina national field test facility. Aust Geomech 49(4):41–48Google Scholar
- Lunne T, Robertson PK, Powell JJM (1997) Cone penetration testing in geotechnical practice. Blackie Academic & Professional, London, UKGoogle Scholar
- Matheron G (1971) The theory of regionalized variables and its applications. Paris: Les Cahiers du Centre de Morphonogie Mathematique de Fontainebleau, Ecole National Superieure des Mines de ParisGoogle Scholar
- Vanmarcke EH (1984) Random fields: analysis and synthesis. MIT Press, Cambridge, MAGoogle Scholar
- White DJ, Westgate ZJ, Tian YH (2014) Pipeline lateral buckling: realistic modelling of geotechnical variability and uncertainty. In: OTCO Committee (ed) Proc Offshore Technology Conference. Offshore Technology Conference, Houston, Texas, USA, OTC 25286Google Scholar