MODELLING UNKNOWN STRUCTURAL SYSTEMS THROUGH THE USE OF NEURAL NETWORKS
This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a repre...
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| Published in | Earthquake engineering & structural dynamics Vol. 25; no. 2; pp. 117 - 128 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
John Wiley & Sons, Ltd
01.02.1996
Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0098-8847 1096-9845 1096-9845 |
| DOI | 10.1002/(SICI)1096-9845(199602)25:2<117::AID-EQE541>3.0.CO;2-A |
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| Abstract | This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a representative chain‐like system under deterministic and stochastic excitations. The neural network based identification method provides very good results for general classes of multi‐degree‐of‐freedom structural systems. The range of validity of the approach is demonstrated, and some application issues are discussed for (a) partially known multi‐degree‐of‐freedom systems and (b) completely unknown systems. |
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| AbstractList | This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a representative chain-like system under deterministic and stochastic excitations. The neural network based identification method provides very good results for general classes of multi-degree-of-freedom structural systems. The range of validity of the approach is demonstrated, and some application issues are discussed for (a) partially known multi-degree-of-freedom-systems and (b) completely unknown systems. This paper explores the potential of using neural networks to identify the internal forces of typical systems encountered in the field of earthquake engineering and structural dynamics. After formulating the identification task as a neural network learning procedure, the method is applied to a representative chain-like system under deterministic and stochastic excitations. The neural network based identification method provides very good results for general classes of multi-degree-of-freedom structural systems. The range of validity of the approach is demonstrated, and some applications issues are discussed for (a) partially known multi-degree-of-freedom systems and (b) completely unknown systems. |
| Author | CHASSIAKOS, A. G. MASRI, S. F. |
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| Keywords | models dynamics Neural network earthquake engineering earthquakes wave damping |
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| References | S. F. Masri and T. K. Caughey, 'A nonparametric identification technique for nonlinear dynamic problems', J. appl. mech. ASME 46, 433-447 (1979). A. G. Chassiakos and S. F. Masri, 'Identification of the internal forces of structural systems using feedforward multilayer networks', Comput. systems eng. 2, 125-134 (1991). P. Ibanez, 'Review of analytical and experimental techniques for improving structural dynamic models', Welding Research Council Bulletin No. 249, June 1979. K. S. Narendra and K. Parthasarathy, 'Identification and control of dynamical systems using neural networks', IEEE trans. neural networks 1, 4-27 (1990). S. F. Masri, A. G. Chassiakos and T. K. Caughey, 'Identification of nonlinear dynamic systems using neural networks', J. appl. mech. ASME 60, 123-133 (1993). S. F. Masri and S. D. Werner, 'An evaluation of a class of practical optimization techniques for structural dynamics applications', Earthquake eng. struct. dyn. 13, 635-649 (1985). J. L. Beck, 'Determining models of structures from earthquake records', Earthquake Engineering Research Laboratory, California Institute of Technology, Pasadena, 1978. S. F. Masri, A. G. Chassiakos and T. K. Caughey, 'Structure-unknown non-linear dynamic systems: identification through neural networks', Smart materials structures 1, 45-56 (1992). H. G. Natke (Ed.), Identification of Vibrating Structures, Springer, Berlin, 1982. J. L. McLelland and D. E. Rumelhart, Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, MA, 1986. D. H. Nguyen and B. Widrow, 'Neural networks for self learning control systems', IEEE control systems mag. 10 (3), 18-23 (1990). 1991; 2 1990; 1 1986; 1 1990; 10 1979; 46 1990 1993; 60 1987 1982 1992 1992; 1 1979 1978 1985; 13 1988 |
| References_xml | – reference: S. F. Masri, A. G. Chassiakos and T. K. Caughey, 'Structure-unknown non-linear dynamic systems: identification through neural networks', Smart materials structures 1, 45-56 (1992). – reference: J. L. McLelland and D. E. Rumelhart, Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, MA, 1986. – reference: S. F. Masri, A. G. Chassiakos and T. K. Caughey, 'Identification of nonlinear dynamic systems using neural networks', J. appl. mech. ASME 60, 123-133 (1993). – reference: K. S. Narendra and K. Parthasarathy, 'Identification and control of dynamical systems using neural networks', IEEE trans. neural networks 1, 4-27 (1990). – reference: P. Ibanez, 'Review of analytical and experimental techniques for improving structural dynamic models', Welding Research Council Bulletin No. 249, June 1979. – reference: H. G. Natke (Ed.), Identification of Vibrating Structures, Springer, Berlin, 1982. – reference: S. F. Masri and T. K. Caughey, 'A nonparametric identification technique for nonlinear dynamic problems', J. appl. mech. ASME 46, 433-447 (1979). – reference: A. G. Chassiakos and S. F. Masri, 'Identification of the internal forces of structural systems using feedforward multilayer networks', Comput. systems eng. 2, 125-134 (1991). – reference: S. F. Masri and S. D. Werner, 'An evaluation of a class of practical optimization techniques for structural dynamics applications', Earthquake eng. struct. dyn. 13, 635-649 (1985). – reference: J. L. Beck, 'Determining models of structures from earthquake records', Earthquake Engineering Research Laboratory, California Institute of Technology, Pasadena, 1978. – reference: D. H. Nguyen and B. Widrow, 'Neural networks for self learning control systems', IEEE control systems mag. 10 (3), 18-23 (1990). – year: 1982 – volume: 1 start-page: 4 year: 1990 end-page: 27 article-title: Identification and control of dynamical systems using neural networks publication-title: IEEE trans. neural networks – volume: 1 year: 1986 – year: 1988 – year: 1987 – volume: 60 start-page: 123 year: 1993 end-page: 133 article-title: Identification of nonlinear dynamic systems using neural networks publication-title: J. appl. mech. ASME – volume: 2 start-page: 125 year: 1991 end-page: 134 article-title: Identification of the internal forces of structural systems using feedforward multilayer networks publication-title: Comput. systems eng. – volume: 46 start-page: 433 year: 1979 end-page: 447 article-title: A nonparametric identification technique for nonlinear dynamic problems publication-title: J. appl. mech. ASME – volume: 1 start-page: 45 year: 1992 end-page: 56 article-title: Structure‐unknown non‐linear dynamic systems: identification through neural networks publication-title: Smart materials structures – volume: 13 start-page: 635 year: 1985 end-page: 649 article-title: An evaluation of a class of practical optimization techniques for structural dynamics applications publication-title: Earthquake eng. struct. dyn. – year: 1978 – year: 1979 – volume: 10 start-page: 18 issue: 3 year: 1990 end-page: 23 article-title: Neural networks for self learning control systems publication-title: IEEE control systems mag. – year: 1990 – year: 1992 |
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| SubjectTerms | Applied sciences Buildings. Public works dynamics Earth sciences Earth, ocean, space Earthquakes, seismology Exact sciences and technology identification Internal geophysics modelling neural nonparametric Stresses. Safety Structural analysis. Stresses systems |
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| Title | MODELLING UNKNOWN STRUCTURAL SYSTEMS THROUGH THE USE OF NEURAL NETWORKS |
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