Technical Papers
Feb 3, 2011

Improved SelfSim for Inverse Extraction of Nonuniform, Nonlinear, and Inelastic Material Behavior under Cyclic Loadings

Publication: Journal of Aerospace Engineering
Volume 25, Issue 2

Abstract

In this paper, an improved self-learning simulation (SelfSim) method is proposed for the inverse extraction of nonuniform, inelastic, and nonlinear material behavior under cyclic loadings. The SelfSim has been used to inversely extract local inelastic and nonlinear behavior of materials using limited global boundary responses. However, the SelfSim with conventional artificial neural network (ANN) models needs ad hoc data processing that frequently interrupts SelfSim training even in training monotonic constitutive behavior. To overcome this problem, an improved SelfSim with a new ANN-based hysteretic model is proposed. In addition, the ANN material model is implemented with considerations of large volume changes and geometric nonlinearity. The new SelfSim shows superior performances in the inverse modeling of complex material behavior under “multiaxial” and “cyclic” stress states. Two simulated numerical tests using a laminated rubber bearing with reinforcing steel shims are used to demonstrate the proposed SelfSim performance. A detailed comparison of the SelfSim with conventional ANN model is also presented. Finally, the improved SelfSim is experimentally verified by extracting nonuniform, nonlinear, and inelastic behavior of metallic material (low carbon SAE 1006 specimen) under cyclic loading. It shows very promising performances for the inverse material characterization of various engineering materials.

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Acknowledgments

This research is supported by the New Faculty Startup Fund and Firestone Faculty Research Program from the College of Engineering of the University of Akron. Authors are grateful to them for their support. They are also thankful for Mr. Brett Bell and Mr. David McVaney for their help with the ARAMIS system and the preparation of tested materials.

References

ABAQUS/Standard, H. (2004). “A general purpose finite element code.” Karlsson & Sorense, Inc: Hibbitt.
Bari, S., and Hassan, T. (2000). “Anatomy of coupled constitutive models for ratcheting simulation.” Int. J. Plast., 16(3–4), 381–409.IJPLER
Choi, Y., Han, C. S., Lee, J. K., and Wagoner, R. H. (2006). “Modeling multi-axial deformation of planar anisotropic elasto-plastic materials, part 1: Theory.” Int. J. Plast.IJPLER, 22(9), 1745–1764.
Fu, Q. W., Hashash, Y. M.A., Jung, S., and Ghaboussi, J. (2007). “Integration of laboratory testing and constitutive modeling of soils.” Comput. Geotech.CGEOEU, 34(5), 330–345.
Ghaboussi, J., Garrett, J., and Wu, X. (1991). “Knowledge-based modeling of material behavior with neural networks.” J. Eng. Mech.JENMDT, ASCE, 117(1), 132–153.
Ghaboussi, J., Pecknold, D. A., Zhang, M. F., and Haj-Ali, R. M. (1998). “Autoprogressive training of neural network constitutive models.” Int. J. Numerical Methods Eng.IJNMBH, 42(1), 105–126.
Hashash, Y. M.A., Jung, S., and Ghaboussi, J. (2004). “Numerical implementation of a neural network based material model in finite element analysis.” Int. J. Numerical Methods Eng.IJNMBH, 59(7), 989–1005.
Hashash, Y. M.A., Marulanda, C., Ghaboussi, J., and Jung, S. (2003). “Systematic update of a deep excavation model using field performance data.” Comput. Geotech.CGEOEU, 30(6), 477–488.
Hashash, Y. M.A., Song, H., Jung, S., and Ghaboussi, J. (2009). “Extracting inelastic metal behaviour through inverse analysis: A shift in focus from material models to material behaviour.” Inverse Problems in Science and Eng.IPSECR, 17(1), 35–50.
Jung, S., and Ghaboussi, J. (2006). “Characterizing rate-dependent material behaviors in self-learning simulation.” Comput. Methods Applied Mech. Eng.CMMECC, 196(1–3), 608–618.
Kim, J. H., Ghaboussi, J., and Elnashai, A. S. (2010). “Mechanical and infomational modeling of steel beam-to-column connections.” Eng. Struct.ENSTDF, 32(2), 449–458.
Koo, G. H., and Lee, J. H. (2007). “Investigation of ratcheting characteristics of modified 9Cr-1 Mo steel by using the Chaboche constitutive model.” Int. J. Pressure Vessels PipingPRVPAS, 84(5), 284–292.
Saleeb, A. F., Arnold, S. M., Castelli, M. G., Wilt, T. E., and Graf, W. (2001). “A general hereditary multimechanism-based deformation model with application to the viscoelastoplastic response of titanium alloys.” Int. J. Plast.IJPLER, 17(10), 1305–1350.
Saleeb, A. F., Gendy, A. S., and Wilt, T. E. (2002). “Parameter-estimation algorithms for characterizing a class of isotropic and anisotropic viscoplastic material models.” Mech. Time-Depend. Mater.MTDMFH, 6(4), 323–362.
Saleeb, A. F., Trowbridge, D. A., Wilt, T. E., Marks, J. R., and Vesely, I. (2006). “Dynamic pre-processing software for the hyperviscoelastic modeling of complex anisotropic biological tissue materials.” Advances in Eng. SoftwareAESODT, 37(9), 609–623.
Saleeb, A. F., Wilt, T. W., Al-Zoubi, N. R., and Gendy, A. S. (2003). “An anisotropic viscoelastoplastic model for composites—Sensitivity analysis and parameter estimation.” Compos. Part B-Eng.CPBEFF, 34(1), 21–39.
Shin, H. S., and Pande, G. N. (2000). “On self-learning finite element codes based on monitored response of structures.” Comput. Geotech.CGEOEU, 27(3), 161–178.
Tsai, C. C. (2007). “Seismic site response and interpretation of dynamic soil behavior from downhole array measurements.” Civil & Environ. Eng., Univ. of Illinois Urbana-Champaign, Urbana, IL.
Wang, J., Levkovitch, V., and Svendsen, B. (2006). “Modeling and simulation of directional hardening in metals during non-proportional loading.” J. Mater. Process. Technol.JMPTEF, 177(1–3), 430–432.
Xia, Z., Ellyin, F., and Meijer, G. (1997). “Mechanical behavior of Al2O3-particle-reinforced 6061 aluminum alloy under uniaxial and multiaxial cyclic loading.” Composites Science and TechnologyCSTCEH, 57(2), 237–248.
Yun, G. J. (2006). “Modeling of hysteretic behavior of beam-column connections based on self-learning simulation.” Dept. Civil and Environ. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL.
Yun, G. J., Ghaboussi, J., and Elnashai, A. (2008a). “A design-variable-based inelastic hysteretic model for beam–column connections.” Earthquake Eng. & Struct. DynamicsIJEEBG, 37(4), 535–555.
Yun, G. J., Ghaboussi, J., and Elnashai, A. S. (2008b). “A new neural network-based model for hysteretic behavior of materials.” Int. J. Numerical Methods Eng.IJNMBH, 73(4), 447–469.
Yun, G. J., Ghaboussi, J., and Elnashai, A. S. (2008c). “Self-learning simulation method for inverse non-linear modeling of cyclic behavior of connections.” Comput. Methods Applied Mech. Eng.CMMECC, 197(33–40), 2836–2857.
Yun, G. J., and Saleeb, A. F. (2009). “An inverse material characterization method for lead rubber bearing under non-uniform cyclic stress states.” 2009 Joint ASCE-ASME-SES Conf. on Mechanics and Materials, Blacksburg, VA.
Zhang, M. (1996). “Neural network material models determined from structural tests.” Dept. Civil and Environ. Eng., Univ. of Illinois at Urbana-Champaign, Champaign, IL, 156.

Information & Authors

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Published In

Go to Journal of Aerospace Engineering
Journal of Aerospace Engineering
Volume 25Issue 2April 2012
Pages: 256 - 272

History

Received: Jan 16, 2010
Accepted: Feb 1, 2011
Published online: Feb 3, 2011
Published in print: Apr 1, 2012

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Authors

Affiliations

Gun Jin Yun, A.M.ASCE [email protected]
Assistant Professor, Dept. of Civil Engineering, The Univ. of Akron, Akron, OH 44325-3905 (corresponding author). E-mail: [email protected]
Atef Saleeb, M.ASCE
Professor, Dept. of Civil Engineering, The Univ. of Akron, Akron, OH 44325-3905.
Shen Shang
Graduate student, Dept. of Civil Engineering, The Univ. of Akron, Akron, OH 44325-3905.
Wieslaw Binienda, F.ASCE
Professor, Dept. of Civil Engineering, The Univ. of Akron, Akron, OH 44325-3905.
Craig Menzemer, M.ASCE
Associate Professor, Dept. of Civil Engineering, The Univ. of Akron, Akron, OH 44325-3905.

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