Nonlinear Civil Structures Identification Using a Polynomial Artificial Neural Network

  • Francisco J. Rivero-Angeles
  • Eduardo Gomez-Ramirez
  • Ruben Garrido
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


Civil structures could undergo hysteresis cycles due to cracking or yielding when subjected to severe earthquake motions or even high wind. System identification techniques have been used in the past years to assess civil structures under lateral loads. The present research makes use of a polynomial artificial neural network to identify and predict, on-line, the behavior of such nonlinear structures. Simulations are carried out using the Loma Prieta and the Mexico City seismic records on two hysteretic models. Afterwards, two real seismic records acquired on a 24-story concrete building in Mexico City are used to test the proposed algorithm. Encouraging results are obtained: fast identification of the weights and fair prediction of the output acceleration.


Mexico City Structural Health Monitoring Seismic Record Civil Structure Hysteretic Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Francisco J. Rivero-Angeles
    • 1
  • Eduardo Gomez-Ramirez
    • 2
  • Ruben Garrido
    • 1
  1. 1.Departamento de Control AutomaticoCentro de Investigacion y de Estudios Avanzados del IPN, CINVESTAVMexico, D.F.Mexico
  2. 2.Laboratorio de Investigacion y Desarrollo, de Tecnologia AvanzadaUniversidad La SalleMexico, D.F.Mexico

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