Structural Parameter Identification Using Interval Functional Link Neural Network

  • Deepti Moyi SahooEmail author
  • S. Chakraverty
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


This paper presents a procedure to identify uncertain structural parameters of multistorey shear buildings by interval functional link neural network. The structural parameters are identified using the response of the structure with both ambient and forced vibration. Here interval functional link neural network has been used to train interval data. The polynomials used in the functional link are Chebyshev polynomial. Different degrees of Chebyshev polynomial is used for training and further it is tested with interval Legendre polynomial using the stored converged weights of ChNN. These polynomials are taken in interval form. It is seen that by using interval functional link neural network the computational time is very less compared to interval neural network. Accordingly example problems of two and five-storey shear buildings have been analyzed for free and forced vibration case to show the efficiency of the IFLNN model.


Structural parameters Chebyshev polynomials ChNN IChNN Shear buildings 


  1. 1.
    Loh CH, Ton IC (1995) A system identification approach to the detection of changes in both linear and non-linear structural parameters. J Earthquake Eng Struct Dynam 24(1):85–97CrossRefGoogle Scholar
  2. 2.
    Katsikadelis JT, Nerantzaki MS (1998) Solving inverse problems by use of the AEM. In: Inverse Problems in Engineering Mechanics, International Symposium on Inverse Problems in Engineering Mechanics, Nagano, Japan, 24–27 March, At Nagano, Japan, pp 593–602CrossRefGoogle Scholar
  3. 3.
    Zhao Q, Sawada T, Hirao K, Nariyuki Y (1995) Localized identification of MDOF structures in the frequency domain. J Earthquake Eng Struct Dynam 24(3):325–338CrossRefGoogle Scholar
  4. 4.
    Datta AK, Shrikhande M, Paul DK (1998) System identification of buildings—a review. In: Proceeding of the 11th Symposium on Earthquake Engineering, University of Roorkee, Roorkee (1998)Google Scholar
  5. 5.
    Udwadia FE, Proskurowski W (1998) A memory matrix-based identification methodology for structural and mechanical systems. J Earthquake Eng Struct Dynam 27:1465–1481CrossRefGoogle Scholar
  6. 6.
    Sanayei M, Mc Clain JAS, Fascetti SW, Santini EM (1999) Parameter estimation incorporating modal data and boundary conditions. J Struct Eng 125(9): 1048–1055CrossRefGoogle Scholar
  7. 7.
    Chakraverty S (2004) Modelling for identification of stiffness parameters of multistorey frame structure from dynamic data. J Sci Ind Res 63:142–148Google Scholar
  8. 8.
    Chakraverty S (2005) Identification of structural parameters of multistorey shear buildings from modal data. Earthquake Eng Struct Dynam 34(6):543–554CrossRefGoogle Scholar
  9. 9.
    Alvin KF, Robertson AN, Reich GW, Park KC (2003) Structural system identification: from reality to models. Comput Struct 81:1149–1176CrossRefGoogle Scholar
  10. 10.
    Facchini L, Betti M, Biagini P (2014) Neural network based modal identification of structural systems through output-only measurement. Comput Struct 138:183–194CrossRefGoogle Scholar
  11. 11.
    Chakraverty S, Sahoo DM (2014) Interval response data based system identification of multi storey shear buildings using interval neural network modelling. Comput Assist Methods Eng Sci 21(2):123–140MathSciNetGoogle Scholar
  12. 12.
    Chakraverty S, Sahoo DM (2015) Fuzzy neural network-based system identification of multi-storey shear buildings. Neural Comput Appl 27(2):1–16Google Scholar
  13. 13.
    Patra JC (2011) Chebyshev neural network-based model for dual-junction solar cells. IEEE Trans Energy Convers 26(1):132–139CrossRefGoogle Scholar
  14. 14.
    Patra JC, Pal RN, Chatterji BN, Panda G (1999) Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans Syst Man Cybern Part B Cybern 29(2):254–262CrossRefGoogle Scholar
  15. 15.
    Patra JC, Kot AC, Chen YQ (2000) Chebyshev functional link artificial neural networks for nonlinear dynamic system identification. In: Proceedings of the IEEE international conference on systems, man and cybernetics, vol 4, pp 2655–2660Google Scholar
  16. 16.
    Patra JC, Kot AC (2002) Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans Syst Man Cybern Part B Cybern 32(4):505–511CrossRefGoogle Scholar
  17. 17.
    Purwar S, Kar IN, Jha AN (2003) On-line system identification using chebyshev neural networks. In: IEEE region 10 annual international conference, proceedings/TENCON, vol 2, pp 1115–1119Google Scholar
  18. 18.
    Xiuchun X, Xiohua J, Yunong Z (2009) An algorithm for designing chebyshev neural network. In: ISECS international colloquium on computing, communication, control and management, pp 206–209Google Scholar
  19. 19.
    Mishra SK, Panda G, Meher S (2010) Chebyshev functional link artificial neural networks for denoising of image corrupted by salt and pepper noise. ACEEE Int J Signal Image Process 1(1):42–46Google Scholar
  20. 20.
    Li M, He Y (2010) Nonlinear system identification using adaptive Chebyshev neural networks. In: Proceedings—2010 IEEE international conference on intelligent computing and intelligent systems, ICIS 2010, 1, vol 5658578, pp 243–247Google Scholar
  21. 21.
    Shaik FA, Purwar S, Pratap B (2011) Real-time implementation of Chebyshev neural network observer for twin rotor control system. Expert Syst Appl 38:13043–13049CrossRefGoogle Scholar
  22. 22.
    Dehuri S (2011) A novel learning scheme for chebyshev functional link neural networks. Adv Artif Neural Syst 2011:1–10CrossRefGoogle Scholar
  23. 23.
    Jiang LL (2012) Chebyshev functional link neural network-based modeling and experimental verification for photovoltaic arrays. In: WCCI 2012 IEEE world congress on computational intelligence, 10–15 June, Brisbane, Australia, pp 1–9Google Scholar
  24. 24.
    Patrício F, Ferreira JA, Oliveira F (2003) On the interval legendre polynomials. J Comput Appl Math 154:215–227MathSciNetCrossRefGoogle Scholar
  25. 25.
    Chakraverty S (2007) Identification of structural parameters of two-storey shear buildings by the iterative training of neural networks. J Architect Sci Rev 50(4):380–384CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Mathematics, School of ScienceO.P. Jindal UniversityRaigarhIndia
  2. 2.Department of MathematicsNational Institute of Technology RourkelaRourkelaIndia

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