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Interval response data based system identification of multi storey shear buildings using interval neural network modelling

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper uses artificial neural network (ANN) technique for the identification of structural parameters of multistorey shear buildings. First, the identification has been done using response of the structure subject to ambient vibration with interval initial condition. Then, forced vibration with horizontal displacement in interval form has been used to investigate the identification procedure. The neural network has been trained by a methodology so as to handle interval data. This is because, in general we may not get the corresponding input and output values exactly (in crisp form) but we may only have the uncertain information of the data. These uncertain data are assumed in term of interval and the corresponding problem of system identification is investigated. The model has been developed for multistorey shear structure and the procedure is tested for the identification of the stiffness parameters of simple example problem using the prior values of the design parameters.
Rocznik
Strony
123--140
Opis fizyczny
Bibliogr. 41 poz., rys., tab., wykr.
Twórcy
  • Department of Mathematics, National Institute of Technology Rourkela-769008, Odisha, India
autor
  • Department of Mathematics, National Institute of Technology Rourkela-769008, Odisha, India
Bibliografia
  • [1] Y. Robert-Nicoud, B. Raphael, I.F. Smith. System identification through model composition and stochastic search. Journal of Computing in Civil Engineering, 19(3): 239–247, 2005.
  • [2] K. Zhang, H. Li, Z. Duan, S.S. Law. A probabilistic damage identification approach for structures with uncertainties under unknown input. Mechanical Systems and Signal Processing, 25(4): 1126–1145, 2011.
  • [3] B. Xu, G. Song, S.F. Masri. Damage detection for a frame structure model using vibration displacement measurement. Structural Health Monitoring, 11(3): 281–292, 2012.
  • [4] Y. Lei, Y. Su, W. Shen. A probabilistic damage identification approach for structure under unknown excitation and with measurement uncertainties. Journal of Applied Mathematics, 2013: 1–7, 2013.
  • [5] P. Nandakumar, K. Shankar. Identification of structural parameters using consistent mass transfer matrix. Inverse Problems in Science and Engineering, 1–22, 2013.
  • [6] M. Billmaier, C. Bucher. System identification based on selective sensitivity analysis: A case-study. Journal of Sound and Vibration, 332(11): 2627–2642, 2013.
  • [7] P. Ibanez. Review of analytical and experimental techniques for improving structural dynamic models. Welding Research Council Bulletin, 249: 1–193, 1979.
  • [8] A.K. Datta, M. Shrikhande, D.K. Paul. System identification of buildings: A review. Proceedings of Eleventh Symposium on Earthquake Engineering, Roorkee, India: University of Roorkee, 1998.
  • [9] P. Yuan, Z. Wu, X. Ma. Estimated mass and stiffness matrices of shear building from modal test data. Earthquake Engineering and Structural Dynamics, 27(5): 415–421, 1998.
  • [10] S.T. Quek, W. Wang, C.G. Koh. System identification of linear MDOF structures under ambient excitation. Earthquake Engineering and Structural Dynamics, 28(1): 61–77, 1999.
  • [11] J.M.W. Brownjohn. Ambient vibration studies for system identification of tall buildings. Earthquake Engineering and Structural Dynamics, 32(1): 71–96, 2003.
  • [12] J.N. Yang, Y. Lei, S. Pan, N. Huang. System identification of linear structures based on Hilbert-Huang spectral analysis, Part 1: Normal modes. Earthquake Engineering and Structural Dynamics, 32(9): 1443–1467, 2003.
  • [13] S. Chakraverty. Identification of structural parameters of multistorey shear buildings from modal data. Earthquake Engineering and Structural Dynamics, 34(6): 543–554, 2005.
  • [14] G. Hegde, R. Sinha. Parameter identification of torsionally coupled shear buildings from earthquake response records. Earthquake Engineering and Structural Dynamics, 37(11): 1313–1331, 2008.
  • [15] E. Khanmirza, N. Khaji, V. J. Majd. Model updating of multistory shear buildings for simultaneous identification of mass, stiffness and damping matrices using two different soft-computing methods. Expert Systems with Applications, 38(5): 5320–5329, 2011.
  • [16] S. Beskhyroun, L. Wotherspoon, Q. T. Ma. System identification of a 13-story reinforced concrete building through ambient and forced vibration. 4th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Greece, June, 1–11, 2013.
  • [17] C.S. Huang, S.L. Hung, C.M. Wen, T.T. Tu. A neural network approach for structural identification and diagnosis of a building from seismic response data. Earthquake Engineering and Structural Dynamics, 32(2): 187–206, 2003.
  • [18] S. Chakraverty. Modelling for identification of stiffness parameters of multi-storey structure from dynamic data. Journal of Scientific and Industrial Research, 63(2): 142–148, 2004.
  • [19] S.F. Masri, A.W. Smyth, A.G. Chassiakos, T.K. Caughey, N.F. Hunter. Application of neural networks for detection of changes in nonlinear systems. Journal of Engineering Mechanics, 126(7): 666–676, 2000.
  • [20] C.B. Yun, E.Y. Bahng. Substructural identification using neural networks. Computers & Structures, 77(1): 41–52, 2000.
  • [21] Z.S. Wu, B. Xu, K. Yokoyama. Decentralized parametric damage based on neural networks. Computers Aided Civil and Infrastructure Engineering, 17(3): 175–184, 2002.
  • [22] B. Xu, Z. Wu, K. Yokoyama. A localized identification method with neural networks and its application to structural health monitoring. Journal of Structural Engineering, 48A: 419–427, 2002.
  • [23] S. Chakraverty, R.K. Sharma, V.P. Singh. Soft-computing approach for Identification of dynamic systems. Journal of New Building Materials and Construction World, 9(2), 2–56, 2003.
  • [24] C.Y. Kao, S.L. Hung. Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Computers and Structures, 81(28–29): 2631–2644, 2003.
  • [25] B. Xu, Z. Wu, G. Chen, K. Yokoyama. Direct identification of structural parameters from dynamic responses with neural networks. Engineering Applications of Artificial Intelligence, 17(8): 931–943, 2004.
  • [26] S. Chakraverty. Identification of structural parameters of two-storey shear buildings by the iterative training of neural networks. Journal Architectural Science Review, 50(4): 380–384, 2007.
  • [27] P. Pillai, S. Krishnapillai. A hybrid neural network strategy for identification of structural parameters. Structure and Infrastructure Engineering, 6(3): 379–391, 2007.
  • [28] A.L. Hong, R. Betti, C.C. Lin. Identification of dynamic models of a building structure using multiple earthquake records. Structural Control and Health Monitoring, 16(2): 178–199, 2009.
  • [29] N. Bakhary, H. Hao, A.J. Deeks. Structure damage detection using neural with multi-stage substructuring. Advances in Structural Engineering, 13(1): 1–16, 2009.
  • [30] S. Zhang, H. Wang, W. Wang. Damage detection in structures using artificial neural networks. International Conference on Artificial Intelligence and Computational Intelligence, Sanya, October, 1: 207–210, 2010.
  • [31] D.G. Pizano. Comparison of frequency response and neural network techniques for system identification of an actively controlled structure. Dyna, 78(170):79–89, 2011.
  • [32] A. Shi, X.H. Yu. Structural damage detection using artificial neural networks and avelet transform. IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings, 7–11, 2012.
  • [33] L. Niu. Monitoring of a frame structure model for damage identification using artificial neural networks. 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT-2012), 0438–0441, 2012.
  • [34] V. Mucenski, M. Trivunic, G. Cirovic, I. Pesko, J. Drazic. Estimation of recycling of multi-storey building structures using artificial neural networks. Journal of Applied Sciences, 10(4): 175–192, 2013.
  • [35] M.Q. Zhang, M. Beer, C.G. Koh. Interval analysis for system identification of linear MDOF structures in the presence of modeling errors. Journal of Engineering Mechanics, 138(11): 1326–1338, 2012.
  • [36] M. Beheshti, A. Berrached, A. De Korvin, C. Hu, O. Sirisaengtaksin. On interval weighted three-layer neural networks. Proceedings of the 31st Annual Simulation Symposium, Boston, MA, April, 188–194, 1998.
  • [37] Z.A. Garczarczyk. Interval neural networks. IEEE International Symposium on Circuits and Systems, Geneva, Switzerland, May, 3: 567–570, 2000.
  • [38] D. Chetwynd, K. Worden, G. Manson. An application of interval-valued neural networks to a regression problem. Proceedings of the Royal Society A, 462(2074): 3097–3114, 2006.
  • [39] H. Okada, T. Matsuse, T. Wada, A. Yamashita. Interval GA for evolving neural networks with interval weights and biases. SICE Annual Conference Ankita University, Ankita, Japan, August, 1542–1545, 2012.
  • [40] S. Chakraverty. Vibration of plates. Taylor and Francis Group, CRC Press, 1–411, 2009.
  • [41] K.H. Lee. First course on fuzzy theory and applications. Springer International Edition, 1–333, 2009.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5fb9b48d-12d1-46f7-bb54-c3f405f4e234
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