Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections

Computer Methods in Applied Mechanics and Engineering

Volumn 197, Issue 33-40, 2008, Pages 2836-2857

  Yun, Gun Jin ^a   Ghaboussi, Jamshid ^b   Elnashai, Amr S ^b  

^a University of Akron   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Beam column connections; Cyclic models; Inverse problem; Neural networks; Nonlinear analysis; Self learning simulation

Indexed keywords

ALGORITHMS; BOUNDARY CONDITIONS; COMPUTER SIMULATION; CYCLIC LOADS; FINITE ELEMENT METHOD; INVERSE PROBLEMS; NEURAL NETWORKS; OPTIMIZATION; STRUCTURAL ANALYSIS;

CYCLIC MODELS; SELF-LEARNING SIMULATION;

JOINTS (STRUCTURAL COMPONENTS);

ALGORITHMS; BOUNDARY CONDITIONS; COMPUTER SIMULATION; CYCLIC LOADS; FINITE ELEMENT METHOD; INVERSE PROBLEMS; JOINTS (STRUCTURAL COMPONENTS); NEURAL NETWORKS; OPTIMIZATION; STRUCTURAL ANALYSIS;

EID: 44749087886     PISSN: 00457825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cma.2008.01.021     Document Type: Article

References (27)

1. A. Elnashai, B. Spencer, D. Kuchma, G. Yang, J. Carrion, Q. Gan, S. Kim, Large and small scale simulations on the multi-axial full-scale sub-structured testing and simulation (MUST-SIM) facility at the University of Illinois at Urbana-Champaign, in: NATO Workshop on Seismic Assessment and Rehabilitation of Existing Reinforced Concrete Buildings Istanbul, Turkey, May 30-June 1, 2005.
2. A.S. Elnashai, B.F. Spencer, D. Kuchma, J. Ghaboussi, Y. Hashash, G. Quan, Multi-axial full-scale sub-structured testing and simulation (MUST-SIM) facility at the University of Illinois at Urbana-Champaign, in: Proceedings of the 13th World Conference on Earthquake Engineering Vancouver, Canada, August 2004.
3. Y.M.A. Hashash, J. Ghaboussi, G.J. Yun, A.S. Elnashai, Development of software framework for MUST-SIM; an integrated computational and experimental simulation facility, in: 13th World Conference on Earthquake Engineering Vancouver, BC, Canada, 2004.
4. Zabaras N., Woodbury K.A., and Raynaud M. Inverse Problems in Engineering - Theory and Practice (1993), American Society of Mechanical Engineers, New York
5. Vanderplaats G.N. Numerical Optimization Techniques for Engineering Design with Applications (1984), McGraw-Hill, New York
6. Furukawa T. Parameter identification with weightless regularization. Int. J. Numer. Methods Engrg. 52 (2001) 219-238
7. Statinkov R.B., and Matusov J.B. Multi-criterion Optimization and Engineering (1995), Chapman & Hall, New York
8. Akkaram S., Beeson D., Agarwal H., and Wiggs G. Inverse modeling technology for parameter identification. Struct. Multidiscip. Optim. 34 (2007) 151-164
9. Hashash Y.M.A., Marulanda C., Ghaboussi J., and Jung S. Systematic update of a deep excavation model using field performance data. Comput. Geotech. 30 (2003) 477-488
10. Shin H.S., and Pande G.N. On self-learning finite element codes based on monitored response of structures. Comput. Geotech. 27 (2000) 161-178
11. Sidarta D., and Ghaboussi J. Constitutive modeling of geomaterials from non-uniform material tests. Comput. Geotech. 22 (1998) 53-71
12. Jung S., and Ghaboussi J. Characterizing rate-dependent material behaviors in self-learning simulation. Comput. Methods Appl. Mech. Engrg. 196 (2006) 608-618
13. Aquino W., and Brigham J.C. Self-learning finite elements for inverse estimation of thermal constitutive models. Int. J. Heat Mass Transf. 49 (2006) 2466-2478
14. Furukawa T., and Hoffman M. Accurate cyclic plastic analysis using a neural network material model. Engrg. Anal. Bound. Elem. 28 (2004) 195-204
15. Furukawa T., and Yagawa G. Implicit constitutive modeling for viscoplasticity using neural networks. Int. J. Numer. Methods Engrg. 43 (1998) 195-219
16. G.J. Yun, J. Ghaboussi, A.S. Elnashai, Neural network-based constitutive model for cyclic behavior of materials, in: The first European Conference on Earthquake Engineering and Seismology Geneva, Switzerland, 2006.
17. Yun G.J., Ghaboussi J., and Elnashai A.S. A new neural network-based model for hysteretic behavior of materials. Int. J. Numer. Methods Engrg. 73 (2008) 447-469
18. Ghaboussi J., Pecknold D.A., Zhang M.F., and Haj-Ali R.M. Autoprogressive training of neural network constitutive models. Int. J. Numer. Methods Engrg. 42 (1998) 105-126
19. G.J. Yun, Modeling of hysteretic behavior of beam-column connections based on self-learning simulation, Ph.D. Thesis, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 2006.
20. Hashash Y.M.A., Jung S., and Ghaboussi J. Numerical implementation of a neural network based material model in finite element analysis. Int. J. Numer. Methods Engrg. 59 (2004) 989-1005
21. Chen W.F., and Toma S. Advanced Analysis of Steel Frames (1994), CRC Press
22. Kishi N., and Chen W.F. Database of Steel Beam-to-Column Connections (1986), School of Civil Engineering, Purdue University, Lafayette, IN
23. K. Weinand, SERICON - Databank on Joints in Building Frames, in: Proceedings of the 1st COST C1 Workshop Strasbourg, 1992.
24. Elnashai A.S., Elghazouli A.Y., and Denesh-Ashtiani F.A. Response of semi-rigid steel frames to cyclic and earthquake loads. J Struct. Engrg. 124 (1998) 857-867
25. Faella C., Piluso V., and Rizzano G. Structural Steel Semi-rigid Connections - Theory, Design and Software (2000), CRC Press LLC
26. Stelmack T.W., Marley M.J., and Gerstle K.H. Analysis and tests of flexibly connected steel frames. J Struct. Engrg. ASCE 112 (1986) 1573-1588
27. Takanashi K., Elnashai A.S., Ohi K., and Elghazouli A.Y. Experimental Behavior of Steel and Composite Frames under Cyclic and Dynamic Loading (1992), University of Tokyo and Imperial College