Prediction of the fundamental period of infilled rc frame structures using artificial neural networks

Fabio Di Trapani, Liborio Cavaleri, Constantinos C. Repapis, Athanasios K. Tsaris, Dimitrios F. Karypidis, Athanasios K. Tsaris, Angeliki Papalou, Panagiotis G. Asteris

Risultato della ricerca: Article

65 Citazioni (Scopus)

Abstract

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.
Lingua originaleEnglish
pagine (da-a)1-12
Numero di pagine12
RivistaComputational Intelligence and Neuroscience
Volume2016
Stato di pubblicazionePublished - 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Neuroscience(all)
  • Mathematics(all)

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    Di Trapani, F., Cavaleri, L., Repapis, C. C., Tsaris, A. K., Karypidis, D. F., Tsaris, A. K., Papalou, A., & Asteris, P. G. (2016). Prediction of the fundamental period of infilled rc frame structures using artificial neural networks. Computational Intelligence and Neuroscience, 2016, 1-12.