Bayesian Approach for Probabilistic Site Characterization Using Cone Penetration Tests
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 139, Issue 2
Abstract
This paper develops a Bayesian approach for probabilistic site characterization (i.e., on both stratigraphy and soil properties) using cone penetration tests (CPTs). The available site information prior to the project (e.g., existing geological maps, geotechnical reports, and local experience) is used in the Bayesian approach as prior knowledge, and it is integrated systematically with results of CPTs that are performed deliberately for the project. The inherent spatial variability of soil is modeled explicitly by random field theory. The proposed approach contains two major components: a Bayesian model class selection method to identify the most probable number of statistically homogenous layers of soil and a Bayesian system identification method to estimate the most probable layer thicknesses and soil properties probabilistically. Equations are derived for the Bayesian approach, and the proposed approach is illustrated using a set of real CPT data obtained from a site in Netherlands. It has been shown that the proposed approach correctly identifies the number and thicknesses/boundaries of the statistically homogenous layers of soil and provides proper probabilistic characterization of soil properties. The Bayesian approach provides a means to identify the statistically homogenous layers progressively by gradually zooming into local differences with improved resolution, and it also contains a mechanism to determine when to stop such zooming. In addition, a sensitivity study is performed to explore the effect of prior knowledge.
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Acknowledgments
The work described in this paper was supported by a Strategic Research Grant from City University of Hong Kong (Project No. 7002695). The financial support is gratefully acknowledged. The authors thank Dr. Siu-Kui Au at City University of Hong Kong for valuable comments and advice on the study.
References
Ang, A. H.-S., and Tang, W. H. (2007). Probability concepts in engineering: Emphasis on applications to civil and environmental engineering, Wiley, New York.
Barker, R. M., Duncan, J. M., Rojiani, K. B., Ooi, P. S. K., Tan, C. K., and Kim, S. G. (1991). “Manuals for the design of bridge foundations.” National Cooperative Highway Research Program (NCHRP) Rep. 343, Transportation Research Board, National Research Council, Washington, DC.
Beck, J. L., and Katafygiotis, L. S. (1998). “Updating models and their uncertainties. I: Bayesian statistical framework.” J. Eng. Mech., 124(4), 455–461.
Beck, J. L., and Yuen, K. V. (2004). “Model selection using response measurements: Bayesian probabilistic approach.” J. Eng. Mech., 130(2), 192–203.
Becker, D. E. (1996). “Limit state design for foundations Part II: Development for national building code of Canada.” Can. Geotech. J., 33(6), 984–1007.
Bleistein, N., and Handelsman, R. (1986). Asymptotic expansions of integrals, Dover, New York.
Cao, Z., Wang, Y., and Au, S. K. (2011). “CPT-Based probabilistic characterization of effective friction angle of sand.” Georisk 2011: Geotech. Risk Assess. Manage., 224, 403–410.
Cheung, R. W. M., and Tang, W. H. (2005). “Realistic assessment of slope reliability for effective landslide hazard management.” Geotechnique, 55(1), 85–94.
Ching, J., Phoon, K. K., and Chen, Y. C. (2010). “Reducing shear strength uncertainties in clays by multivariate correlations.” Can. Geotech. J., 47(1), 16–33.
Christian, J. T., and Baecher, G. B. (2011). “Unresolved problems in geotechnical risk and reliability.” Georisk 2011: Geotech. Risk Assess. Manage., 224, 50–63.
Fenton, G. (1999a). “Estimation for stochastic soil models.” J. Geotech. Geoenviron. Eng., 125(6), 470–485.
Fenton, G. (1999b). “Random field modeling of CPT data.” J. Geotech. Geoenviron. Eng., 125(6), 486–498.
Fenton, G., and Griffiths, D. V. (2008). Risk assessment in geotechnical engineering, Wiley, Hoboken, NJ.
Fugro (2004). “Axial pile capacity design method for offshore driven piles in sand.” Rep. to American Petroleum Institute, No. P1003, Fugro, Houston.
Gilbert, R. B., and Tang, W. H. (1995). “Model uncertainty in offshore geotechnical reliability.” Proc., 27th Offshore Technology Conf., Society of Petroleum Engineers, Houston, 557–567.
Gull, S. F. (1988). “Bayesian inductive inference and maximum entropy.” Maximum entropy and Bayesian methods, J. Skilling, ed., Kulwer Academic, Boston, 53–74.
Holtz, W. G. (1973). “The relative density approach—Uses, testing requirements, reliability, and shortcomings.” Proc., Symposium on the Evaluation of Relative Density and Its Role in Geotechnical Projects Involving Cohesionless Soils, ASTM, Philadelphia, 5–17.
Jaksa, M. B. (1995). “The influence of spatial variability on the geotechnical design properties of a stiff, overconsolidated clay.” Ph.D. thesis, Univ. of Adelaide, Adelaide, Australia.
Japanese Geotechnical Society. (2006). Principles for foundation designs grounded on a performance-based design concept, Japanese Geotechnical Society, Tokyo.
Juang, C. H., Rosowsky, D. V., and Tang, W. H. (1999). “Reliability-based method for assessing liquefaction potential of soils.” J. Geotech. Geoenviron. Eng., 125(8), 684–689.
Jung, B. C., Gardoni, P., and Biscontin, G. (2008). “Probabilistic soil identification based on cone penetration tests.” Geotechnique, 58(7), 591–603.
Kulhawy, F. H., and Mayne, P. W. (1990). “Manual on estimating soil properties for foundation design.” Rep. EL 6800, Electric Power Research Institute, Palo Alto, CA.
Lumb, P. (1966). “The variability of natural soils.” Can. Geotech. J., 3(2), 74–97.
Mackay, D. J. C. (1992). “Bayesian interpolation.” Neural Comput., 4(3), 415–447.
MATLAB [Computer software]. Natick, MA, MathWorks.
Mayne, P. W., Christopher, B. R., and DeJong, J. (2002). “Subsurface investigations—Geotechnical site characterization.” Rep. No. FHWA NHI-01-031, Federal Highway Administration, U.S. Dept. of Transportation, Washington, D.C.
Miranda, T., Gomes Correia, A., and Ribeiro e Sousa, L. (2009). “Bayesian methodology for updating geomechanical parameters and uncertainty quantification.” Int. J. Rock Mech. Min. Sci., 46(7), 1144–1153.
Najjar, S. S., and Gilbert, R. B. (2009). “Importance of lower-bound capacities in the design of deep foundations.” J. Geotech. Geoenviron. Eng., 135(7), 890–900.
Niazi, F. S., and Mayne, P. W. (2010). “Evaluation of EURIPIDES pile load tests response from CPT data.” Int. J. Geoeng. Case Hist., 1(4), 367–386.
Orr, T. L. L., and Farrell, E. R. (1999). Geotechnical design to Eurocode 7, Springer, London.
Paikowsky, S. G., et al. (2004). “Load and resistance factor design (LRFD) for deep foundations.” National Cooperative Highway Research Program (NCHRP) Rep. 507, Transportation Research Board, National Research Council, Washington, DC.
Paikowsky, S. G., Canniff, M. C., Lesny, K., Kisse, A., Amatya, S., and Muganga, R. (2010). “LRFD design and construction of shallow foundations for highway bridge structures.” NCHRP Rep. 651, Transportation Research Board, Washington, DC.
Papadimitriou, C., Beck, J. L., and Katafygiotis, L. S. (1997). “Asymptotic expansions for reliability and moments of uncertain systems.” J. Eng. Mech., 123(12), 1219–1229.
Phoon, K. K., and Kulhawy, F. H. (1999). “Characterization of geotechnical variability.” Can. Geotech. J., 36(4), 612–624.
Phoon, K. K., Kulhawy, F. H., and Grigoriu, M. D. (1995). “Reliability-based design of foundations for transmission line structures.” Rep. TR-105000, Electric Power Research Institute, Palo Alto, CA.
Phoon, K. K., Quek, S. T., and An, P. (2003). “Identification of statistically homogeneous soil layers using modified Bartlett statistics.” J. Geotech. Geoenviron. Eng., 129(7), 649–659.
Vanmarcke, E. H. (1977). “Probabilistic modeling of soil profiles.” J. Geotech. Eng., 103(11), 1227–1246.
Vanmarcke, E. H. (1983). Random fields: Analysis and synthesis, MIT Press, Cambridge, MA.
Wang, Y. (2011). “Reliability-based design of spread foundations by Monte Carlo simulations.” Geotechnique, 61(8), 677–685.
Wang, Y., Au, S. K., and Cao, Z. (2010). “Bayesian approach for probabilistic characterization of sand friction angles.” Eng. Geol., 114(3–4), 354–363.
Wang, Y., Au, S. K., and Kulhawy, F. H. (2011). “Expanded reliability-based design approach for drilled shafts.” J. Geotech. Geoenviron. Eng., 137(2), 140–149.
Yan, W. M., Yuen, K. V., and Yoon, G. L. (2009). “Bayesian probabilistic approach for the correlations of compression index for marine clays.” J. Geotech. Geoenviron. Eng., 135(12), 1932–1940.
Yuen, K. V. (2010). “Recent development of Bayesian model class selection and applications in civil engineering.” Struct. Saf., 32(5), 338–346.
Zhang, J., Zhang, L. M., and Tang, W. H. (2009). “Bayesian framework for characterizing geotechnical model uncertainty.” J. Geotech. Geoenviron. Eng., 135(7), 932–940.
Zhang, L. M., Tang, W. H., Zhang, L. L., and Zheng, J. G. (2004). “Reducing uncertainty of prediction from empirical correlations.” J. Geotech. Geoenviron. Eng., 130(5), 526–534.
Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 139 • Issue 2 • February 2013
Pages: 267 - 276
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jul 11, 2011
Accepted: May 1, 2012
Published online: May 3, 2012
Published in print: Feb 1, 2013
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