Nonlinear civil structures identification using a polynomial artificial neural network

  • Authors:

  • Francisco J. Rivero-Angeles;Eduardo Gomez-Ramirez;Ruben Garrido

  • Affiliations:

  • Departamento de Control Automatico, Centro de Investigacion y de Estudios Avanzados del IPN, CINVESTAV, Mexico, D.F., Mexico;Laboratorio de Investigacion y Desarrollo, de Tecnologia Avanzada, Universidad La Salle, Mexico, D.F., Mexico;Departamento de Control Automatico, Centro de Investigacion y de Estudios Avanzados del IPN, CINVESTAV, Mexico, D.F., Mexico

  • Venue:
  • CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.