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

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

Research output: Contribution to journalArticlepeer-review

96 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalComputational Intelligence and Neuroscience
Publication statusPublished - 2016

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Neuroscience
  • General Mathematics


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