Skip to main content
SpringerLink
Log in

The integration of numerical modeling and physical measurements through inverse analysis in geotechnical engineering

  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

In many geotechnical problems, boundary measurements of loads and displacements are known while the underlying soil behavior is unknown. Inverse analysis techniques are employed to quantify the underlying material behavior. The most widely used inverse analysis approaches are parameter optimization methods in which unknown properties of a material constitutive model are adjusted until the calculated displacements or forces match the measurements. The main limitation of the inverse analysis parameter optimization methods is the need for pre-defined constitutive models that do not benefit from fundamentally new information on material behavior. This paper describes the application of a new inverse analysis approach called SelfSim for extracting soil constitutive behavior via a neural network (NN) material model. This new type of inverse analysis procedure allows the user to focus on the underlying material behavior instead of a specific material constitutive model. The proposed approach is demonstrated for three geotechnical engineering problems: (1) laboratory triaxial tests with frictional loading plates, (2) deformations around a deep excavation and (3) seismic site response from a downhole array.

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

Similar content being viewed by others

References

  • Anderson, D.G. and Tang, Y.K. (1989). “Summary of soil characterization program for the Lotung large-scale seismic experiment.” NP-6154, Electric Power Research Institute, Palo Alto, Calif.

    Google Scholar 

  • Dafalias, Y.F. and Popov, E.P. (1975). “A model for nonlinearly hardening materials for complex loading.” Acta Mech., Vol. 21, No. 3, pp. 173–192.

    Article  MATH  Google Scholar 

  • Darragh, R., Graizer, V., and Shakal, A. (1997). “Site Characterization and Site Response Effects at CSMIP Stations: Tarzana and La Cienega Near the Santa Monica Freeway (I-10).” OSMS 96-07, Calif. Div. Mines and Geology.

  • Drucker, D.C. and Prager, W. (1952). “Soil mechanics and plastic analysis or limit design.” Q. Applied Mathematics, Vol. 10, No. 2, pp. 157–175.

    MATH  MathSciNet  Google Scholar 

  • Elgamal, A., Lai, T., Yang, Z., and He, L. (2001) “Dynamic soil properties, seismic downhole arrays and applications in practice.” 4th International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics, San Diego, California, USA.

  • Finno, R. J. and Calvello, M. (2005). “Supported excavations: Observational method and inverse modeling.” Journal of Geotechnical and Geoenvironmental Engineering, Vol. 131, No. 7, pp. 826–836.

    Article  Google Scholar 

  • Fu, Q. (2007). Integration of Soil BehavioR From Laboratory Specimens Subjected to Non-uniform Loading Conditions, Ph.D. thesis.

  • Fu, Q., Hashash, Y. M. A., and Ghaboussi, J. (2007) “Non-uniformity of stresses within a sand specimen in triaxial shear.” Tenth International Symposium on Numerical Models in Geomechanics, Rhodes, Greece.

  • Fu, Q., Hashash, Y. M. A., Jung, S., and Ghaboussi, J. (2007b). “Integration of laboratory testing and constitutive modeling of soils.” Computer and Geotechnics, Vol. 34, No. 5, pp. 330–345.

    Article  Google Scholar 

  • Furukawa, T., Sugata, T., Yoshimura, S., and Hoffman, M. (2002). “An automated system for simulation and parameter identification of inelastic constitutive models.” Computer methods in applied mechanics and engineering, Vol. 191, pp. 2235–2260.

    Article  MATH  Google Scholar 

  • Furukawa, T. and Yagawa, G. (1997). “Inelastic constitutive parameter identification using an evolutionary algorithm with continuous individuals.” International Journal for Numerical Methods in Engineering, Vol. 40, pp. 1071–1090.

    Article  MATH  Google Scholar 

  • Gavrus, A., Massoni, E., and Chenot, J. L. (1996). “An inverse analysis using a finite element model for identification of rheological parameters.” Journal of Materials Processing Technology, Vol. 60, pp. 447–454.

    Article  Google Scholar 

  • Gelin, J. C. and Ghouati, O. (1994). “An inverse method for determining viscoplastic properties of aluminum alloys.” Journal of Materials Processing Technology, Vol. 45, pp. 435–440.

    Article  Google Scholar 

  • Ghaboussi, J., Garrett, J. H., and Wu, X. (1991). “Knowledge-based modeling of material behavior with neural networks.” Journal of Engineering Mechanics-Asce, Vol. 117, No. 1, pp. 132–153.

    Article  Google Scholar 

  • Ghaboussi, J., Pecknold, D. A., Zhang, M. F., and Haj-Ali, R. M. (1998). “Autoprogressive training of neural network constitutive models.” International Journal for Numerical Methods in Engineering, Vol. 42, No. 1, pp. 105–126.

    Article  MATH  Google Scholar 

  • Ghaboussi, J. and Sidarta, D. E. (1998). “New nested adaptive neural networks (nann) for constitutive modeling.” International Journal of Computers and Geotechnics, Vol. 22, No. 1, pp. 29–52.

    Article  Google Scholar 

  • Goh, A. T. C. (1996). “Pile driving records reanalyzed using neural networks.” Journal of Geotechnical Engineering, Vol. 122, No. 6, pp. 492–495.

    Article  Google Scholar 

  • Graizer, V., Shakal, A., and Hipley, P. (2000) “Recent data recorded from downhole geotechnical arrays.” SMIP 2000 Seminar on Utilization of Strong Motion Data, Sacramento, California, USA.

  • Gusel, L. and Brezocnik, M. (2006). “Modeling of impact toughness of cold formed material by genetic programming.” Computational Materials Science, Vol. 37, No. 4, pp. 476–482.

    Article  Google Scholar 

  • Hashash, Y. M. A., Jung, S., and Ghaboussi, J. (2004). “Numerical implementation of a neural network based material model in finite element analysis.” International Journal for Numerical Methods in Engineering, Vol. 59, No. 7, pp. 989–1005.

    Article  MATH  Google Scholar 

  • Hashash, Y.M.A., Marulanda, C., Ghaboussi, J., and Jung, S. (2003). “Systematic update of a deep excavation model using field performance data.” Computers and Geotechnics, Vol. 30, No. 6, pp. 477–488.

    Article  Google Scholar 

  • Hashash, Y.M.A., Marulanda, C., Ghaboussi, J., and Jung, S. (2006). “A novel approach to integration of numerical modeling and field observations for deep excavations.” Journal of Geotechnical and Geoenvironmental Engineering, Vol. 123, No. 8, pp. 1019–1031.

    Article  Google Scholar 

  • Hertz, J., Palmer, R.G., and Krogh, A. (1991). Introduction to the theory of neural computation, Addison-Wesley Pub. Co., Redwood City Calif.

    Google Scholar 

  • Holman, T.P. (2005). “Generalized small strain response of soft Chicago glacial clays.” Northwestern University, Evanston, IL.

    Google Scholar 

  • Jamiolkowski, M., Ladd, C.C., Germaine, J.T., and Lancelotta, R. (1995). “New developments in field and laboratory testing of soils.” Proc. 11th Int. Conf. on Soil Mech. and Foundation Eng., San Francisco, CA, pp. 57–153.

  • Jan, J.C., Hung, S.-L., Chi, S.Y., and Chern, J.C. (2002). “Neural network forecast model in deep excavation.” Journal of Computing in Civil Engineering, Vol. 16, No. 1, pp. 59–65.

    Article  Google Scholar 

  • Jung, S. (2004). Field Calibration of Time-dependent Behavior in Segmental Bridges using Self-learning Simulation, Ph.D. thesis, University of Illinois at Urbana-Champaign, Urbana.

    Google Scholar 

  • Kiefa, M. A. A. (1998). “General Regression Neural Networks for Driven Piles in Cohesionless Soils.” Journal of Geotechnical and Geoenvironmental Engineering, Vol. 124, No. 12, pp. 1177–1185.

    Article  Google Scholar 

  • Kim, M. K. and Lade, P. V. (1988). “Single hardening constitutive model for frictional materials: I. Plastic potential function.” Computers and Geotechnics, Vol. 5, No. 4, pp. 307–324.

    Article  Google Scholar 

  • Kwok, A. O. L., Stewart, J. P., and Hashash, Y. M. A. (2008). “Nonlinear ground response analysis of Turkey Flat shallow stiff soil site to strong ground motion.” Bulletin of Seismological Society of America, accepted.

  • Kwok, A. O. L., Stewart, J. P., Hashash, Y. M. A., Matasovic, N., Pyke, R., Wang, Z., and Yang, Z. (2007). “Use of exact solutions of wave propagation problems to guide implementation of nonlinear, time-domain ground response analysis routines.” ASCE Journal of Geotechnical and Geoenvironmental Engineering, Vol. 133, No. 11, pp. 1337–1481.

    Article  Google Scholar 

  • Lade, P. V. (1977). “Elasto-plastic stress-strain theory for cohesionless soil with curved yield surfaces.” International Journal of Solids and Structures, Vol. 13, No. 11, pp. 1019–1035.

    Article  MATH  Google Scholar 

  • Lade, P. V. and Kim, M. K. (1988a). “Single hardening constitutive model for frictional materials. II. Yield criterion and plastic work contours.” Computers and Geotechnics, Vol. 6, No. 1, pp. 13–29.

    Article  Google Scholar 

  • Lade, P. V. and Kim, M. K. (1988b). “Single hardening constitutive model for frictional materials. III. Comparisons with experimental data.” Computers and Geotechnics, Vol. 6, No. 1, pp. 31–47.

    Article  Google Scholar 

  • Mahnken, R. and Stein, E. (1994). “The identification of parameters for visco-plastic models via finite-element methods and gradient methods.” Modelling and Simulation in Materials Science and Engineering, Vol. 2, pp. 597–616.

    Article  Google Scholar 

  • Marulanda, C. (2005). Integration of Numerical Modeling and Field Observations of Deep Excavations, Ph.D. Thesis, University of Illinois at Urbana-Champaign, Urbana.

    Google Scholar 

  • Mast, P. W., Nash, G. E., Michopoulos, J. G., Thomas, R., Badaliance, R., and Wolock, I. (1995). “Characterization of strain-induced damage in composites based on the dissipated energy density part I. Basic scheme and formulation.” Theoretical and Applied Fracture Mechanics, Vol. 22, pp. 71–96.

    Article  Google Scholar 

  • Mroz, Z. (1967). “ON the description of anisotropic work hardening.” Jour. Mech. Phys. Solids, Vol. 15, pp. 163–175.

    Article  Google Scholar 

  • Nova, R. (1988). “Sinfonietta classica: an exercise on classical soil modeling.” Proc., Symposium on constitutive equations for granular non-cohesive soils, Rotterdam.

  • Pidaparti, R. and Palakal, M. (1993). “Material model for composites using neural networks.” AIAA Journal, Vol. 31, pp. 1533–1535.

    Article  Google Scholar 

  • Prevost, J. H. (1977). “Mathematical modeling of monotonic and cyclic undrained clay behavior.” International Journal of Numerical and Analytical Methods in Geomechanics, Vol. 1, No. 2, pp. 195–216.

    Article  MATH  Google Scholar 

  • Reed, R. D. and Marks, R. J. (1999). Neural smithing: supervised learning in feedforward artificial neural networks, The MIT Press, Cambridge, Mass.

    Google Scholar 

  • ROSRINE. “Resolution of Site Response Issues from the Northridge Earthquake.” http://geoinfo.usc.edu/rosrine/.

  • Saada, A. S. (1988). “Hollow Cylinder Torsional Devices: Their Advantages and Limitations.” Advanced Triaxial Testing of Soil and Rock, R. C. C. Robert T. Donaghe, and Marshall L. Silver, ed., American Society for Testing and Materials, Philadelphia, pp. 766–795.

    Google Scholar 

  • Saucier, C. L. and Lade, P. V. “Torsion shear tests on solid cylindrical soil specimens for neural network modeling.” 13th ASCE Engineering Mechanics Conference, The Johns Hopkins University.

  • Schnur, D. S. and Zabaras, N. (1992). “An inverse method for determining elastic material properties and a material interface.” International Journal for Numerical Methods in Engineering, Vol. 33, pp. 2039–2057.

    Article  MATH  Google Scholar 

  • Schofield, A. and Wroth, C. P. (1968). Critical state soil mechanics, McGraw-Hill, London.

    Google Scholar 

  • Shin, H. S. and Pande, G. N. (2000). “On self-learning finite element codes based on monitored response of structures.” Computers and Geotechnics, Vol. 27, No. 7, pp. 161–178.

    Article  Google Scholar 

  • Sidarta, D., and Ghaboussi, J. (1998). “Modelling constitutive behavior of materials from non-uniform material tests.” Computers and Geotechnics, Vol. 22, No. 1, pp. 53–71.

    Article  Google Scholar 

  • Stewart, J. P., Kwok, O.-L. A., Hashash, Y. M. A., Matasovic, N., Pyke, R., Wang, Z., and Yang, Z. “Overcoming hurdles that limit the application of nonlinear seismic ground response analysis in engineering practice.” Fifth National Seismic Conference on Bridges and Highways, San Francisco.

  • Tsai, C.-C. (2007). “Seismic site response and extraction of dynamic soil behavior from downhole array measurements,” Ph.D. thesis, University of Illinois, Urbana.

    Google Scholar 

  • Tsai, C.-C. and Hashash, Y.M.A. (2006) “A novel framework for extracting dynamic soil behavior from downhole array data.” Eigth National Conference on Earthquake Engineering, San Francisco, CA.

  • Tsai, C.-C., and Hashash, Y.M.A. (2007) “An inverse analysis approach to extract dynamic nonlinear soil behavior from downhole array data.” 4th International Conference on Earthquake Geotechnical Engineering, Thessaloniki, Greece.

  • Tsai, C.-C. and Hashash, Y.M.A. (2008a). “Learning of dynamic soil behavior from downhole arrays.” ASCE Journal of Geotechnical and Geoenvironmental Engineering, submitted for review.

  • Tsai, C.-C. and Hashash, Y.M.A. (2008b). “A novel framework integrating downhole array data and site response analysis to extract dynamic soil behavior.” Soil Dynamics and Earthquake Engineering, Vol. 28,Issue 3, pp/ 181–197.

    Article  Google Scholar 

  • Vermeer, P. A. (1978). “A double hardening model for sand.” Geotechnique, Vol. 28, pp. 413–433.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Y. M. A. Hashash & H. Song

Authors
  1. Y. M. A. Hashash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. M. A. Hashash.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hashash, Y.M.A., Song, H. The integration of numerical modeling and physical measurements through inverse analysis in geotechnical engineering. KSCE J Civ Eng 12, 165–176 (2008). https://doi.org/10.1007/s12205-008-0165-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-008-0165-2

Keywords

Search

Navigation