Shallow foundation response variability due to soil and model parameter uncertainty

Abstract

Geotechnical uncertainties may play crucial role in response prediction of a structure with substantial soil-foundation-structure-interaction (SFSI) effects. Since the behavior of a soil-foundation system may significantly alter the response of the structure supported by it, and consequently several design decisions, it is extremely important to identify and characterize the relevant parameters. Moreover, the modeling approach and the parameters required for the modeling are also critically important for the response prediction. The present work intends to investigate the effect of soil and model parameter uncertainty on the response of shallow foundation-structure systems resting on dry dense sand. The SFSI is modeled using a beam-on-nonlinear-winkler-foundation (BNWF) concept, where soil beneath the foundation is assumed to be an assembly of discrete, nonlinear elements composed of springs, dashpots and gap elements. The sensitivity of both soil and model input parameters on shallow foundation responses are investigated using first-order second-moment (FOSM) analysis and Monte Carlo simulation through Latin hypercube sampling technique. It has been observed that the degree of accuracy in predicting the responses of the shallow foundation is highly sensitive soil parameters, such as friction angle, Poisson’s ratio and shear modulus, rather than model parameters, such as stiffness intensity ratio and spring spacing; indicating the importance of proper characterization of soil parameters for reliable soil-foundation response analysis.

Keywords

shallow foun dation / sensitivity analysis / centrifuge data / first-order-second-moment (FOSM) method / parameter uncertainty

References

[1] Lacasse S, Nadim F. Uncertainties in characterizing soil properties. In: Uncertainty in the Geologic Environment: From Theory to Practic, Proceedings of Uncertainty 96. Madison, Wisconsin, July 31-August 3, 1996, New York, USA, ASCE Geotechnical Special Publication.1996, 58: 49–75

[2] Chakraborty S, Dey S S. Stochastic finite-element simulation of random structure on uncertain foundation under random loading. International Journal of Mechanical Sciences, 1996, 38(11): 1209–1218

[3] Foye K C, Salgado R, Scott B. Assessment of variable uncertainties for reliability based design of foundations. Journal of Geotechnical and Geoenviornmental Engineering. 2006, 132(9): 1197-1207

[4] Jones A L, Kramer S L, Arduino P. Estimation of uncertainty in geotechnical properties for performance-based earthquake engineering. Tech. Rep. 2002/16, Pacific Earthquake Engineering Research Center, PEER, 2002

[5] Lumb P. The variability of natural soils. Engineering Structures, 1966, 3: 74-97

[6] Lutes L D, Sarkani S, Jin S. Response variability of an SSI system with uncertain structural and soil properties. Canadian Geotechnical Journal, 2000, 22(6): 605-620

[7] Na U J, Chaudhuri S R, Shinozuka M. Probabilistic assessment for seismic performance of port structures. Soil Dynamics and Earthquake Engineering, 2008, 28(2): 147-158

[8] Chaudhuri S R, Gupta V K. Variability in seismic response of secondary systems due to uncertain soil properties. Engineering Structures, 2002, 24(12): 1601-1613

[9] Raychowdhury P. Effect of soil parameter uncertainty on seismic demand of low rise steel buildings on dense sand. Soil Dynamics and Earthquake Engineering, 2009, 29(10): 1367-1378

[10] Raychowdhury P, Hutchinson T C. Sensitivity of shallow foundation response to model input parameters. Journal of Geotechnical and Geoenvironmental Engineering, 2010, 136(3): 538-541

[11] Ronold K O, Bjerager P. Model uncertainty representation in geotechnical reliability analysis. Journal of Geotechnical Engineering, 1992, 118(3): 363-376

[12] Boulanger R W, Curras C J, Kutter B L, Wilson D W, Abghari A. Seismic soil-pile-structure interaction experiments and analyses. Journal of Geotechnical and Geoenvironmental Engineering, 1999, 125(9): 750-759

[13] Raychowdhury P, Hutchinson T C. (2008) Nonlinear material models for Winkler-based shallow foundation response evaluation. In: GeoCongress 2008, Characterization, Monitoring,and Modeling of GeoSystems, March 9-12, 2008, New Orleans, LA, ASCE Geotechnical Special Publication No. 179, pp 686-693

[14] Raychowdhury P, Hutchinson T C. Performance evaluation of a nonlinear Winkler-based shallow foundation model using centrifuge test results. Earthquake Engineering & Structural Dynamics, 2009, 38(5): 679-698

[15] Terzaghi K. Theoretical Soil Mechanics. New York: J Wiley, 1943

[16] Meyerhof G G. Some recent research on the bearing capacity of foundations. Canadian Geotechnical Journal, 1963, 1(1): 16-26

[17] Gazetas G. Foundation Engineering Handbook. Fang H Y ed. Van NostrandRienhold, 1991

[18] Gajan, 2003a;

[19] Gajan, 2003b;

[20] Gajan, 2006

[21] EPRI. Manual on Estimating Soil Properties for Foundation Design. Electric Power Research Institute, Palo Alto, California, 1990

[22] Gajan S, Hutchinson T C, Kutter B, Raychowdhury P, Ugalde J A, Stewart J P. Numerical models for the analysis and performance-based design of shallow foundations subjected to seismic loading. PEER Data Report, Pacific Earthquake Engineering Research Center (PEER), 2008

[23] Raychowdhury P. Nonlinear Winkler-based shallow foundation model for performance assessment of seismically loaded structures. Dissertation for the Doctoral Degree. San Diego: University of California, 2008

[24] ATC-40. Seismic Evaluation and Retrofit of Concrete Buildings. Applied Technology Council (ATC), Redwood City, California, 1996

[25] Harden C W, Hutchinson T C, Martin G R, Kutter B L. Numerical modeling of the nonlinear cyclic response of shallow foundations. Tech. Rep. 2005/04, Pacific Earthquake Engineering Research Center, PEER, 2005

[26] OpenSees. Open System for Earthquake Engineering Simulation: OpenSees. Pacific Earthquake Engineering Research Center (PEER), University of California, Berkeley, 2008 (http://opensees.berkeley.edu)

[27] McKay M D, Beckman R J, Conover W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 1979, 21(2): 239-245

[28] Iman R L, Conover W J. A distribution-free approach to including rank correlation among input variables. Bulletin of the Seismic Society of America, 1982, 11(3): 311-334

[29] Stein M. Large sample properties of simulations using Latin hypercube sampling. Technometrics, 1987, 29(2): 143-151

[30] Jindal S. Shallow foundation response analysis: a parametric study. Master’s thesis. Kanpur: Indian Institute of Technology Kanpur, 2011

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg