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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 5104907, 12 pages
Research Article

Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

1Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, Greece
2Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
3Department of Civil Engineering, Piraeus University of Applied Sciences, 250 Thivon and Petrou Ralli Street, Aigaleo, 122 44 Athens, Greece
4Department of Civil Engineering, Technological Educational Institute of Western Greece, 26334 Patra, Greece

Received 29 June 2015; Accepted 29 September 2015

Academic Editor: Christian W. Dawson

Copyright © 2016 Panagiotis G. Asteris et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.