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.
Keywords
- 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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Rivero-Angeles, F.J., Gomez-Ramirez, E., Garrido, R. (2005). Nonlinear Civil Structures Identification Using a Polynomial Artificial Neural Network. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_15
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DOI: https://doi.org/10.1007/11578079_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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