Abstract
Severe earthquake motions could make civil structures to undergo hysteretic cycles and crack or yield their resistant elements. The present research proposes the use of a polynomial artificial neural network to identify and predict, on-line, the behavior of such nonlinear systems. Predictions are carried out first on theoretical hysteretic models and later using two real seismic records acquired on a 24-story concrete building in Mexico City. Only two cycles of movement are needed for the identification process and the results show fair prediction of the acceleration output.
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Rivero-Angeles, F.J., Gomez-Ramirez, E. (2007). Acceleration Output Prediction of Buildings Using a Polynomial Artificial Neural Network. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Hybrid Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37421-3_22
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DOI: https://doi.org/10.1007/978-3-540-37421-3_22
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