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Applied Computational Intelligence and Soft Computing
Volume 2017, Article ID 3508189, 10 pages
https://doi.org/10.1155/2017/3508189
Research Article

Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map

1Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
2Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
3Civil and Environmental Engineering Department, Illinois Institute of Technology, Chicago, IL 60616, USA
4SAMA Technical and Vocational Training College, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran

Correspondence should be addressed to Łukasz Sadowski; lp.ude.rwp@ikswodas.zsakul

Received 6 January 2017; Accepted 13 April 2017; Published 15 May 2017

Academic Editor: Erich Peter Klement

Copyright © 2017 Mehdi Nikoo 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.

Citations to this Article [7 citations]

The following is the list of published articles that have cited the current article.

  • Mehdi Nikoo, Łukasz Sadowski, and Mohammad Nikoo, “Prediction of the Corrosion Current Density in Reinforced Concrete Using a Self-Organizing Feature Map,” Coatings, vol. 7, no. 10, pp. 160, 2017. View at Publisher · View at Google Scholar
  • Faezehossadat Khademi, Mahmoud Akbari, and Mehdi Nikoo, “Displacement Determination of Concrete Reinforcement Building using Data-Driven models,” International Journal of Sustainable Built Environment, 2017. View at Publisher · View at Google Scholar
  • Panagiotis G. Asteris, Saeed Nozhati, Mehdi Nikoo, Liborio Cavaleri, and Mohammad Nikoo, “Krill herd algorithm-based neural network in structural seismic reliability evaluation,” Mechanics of Advanced Materials and Structures, pp. 1–8, 2018. View at Publisher · View at Google Scholar
  • Liborio Cavaleri, Panagiotis G. Asteris, Pandora P. Psyllaki, Maria G. Douvika, Athanasia D. Skentou, and Nikolaos M. Vaxevanidis, “Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks,” Applied Sciences, vol. 9, no. 14, pp. 2788, 2019. View at Publisher · View at Google Scholar
  • Panagiotis G. Asteris, Ioannis Argyropoulos, Liborio Cavaleri, Hugo Rodrigues, Humberto Varum, Job Thomas, and Paulo B. Lourenço, “Masonry Compressive Strength Prediction Using Artificial Neural Networks,” Transdisciplinary Multispectral Modeling and Cooperation for the Preservation of Cultural Heritage, vol. 961, pp. 200–224, 2019. View at Publisher · View at Google Scholar
  • Maria Apostolopoulou, Danial J. Armaghani, Asterios Bakolas, Maria G. Douvika, Antonia Moropoulou, and Panagiotis G. Asteris, “Compressive strength of natural hydraulic lime mortars using soft computing techniques,” Procedia Structural Integrity, vol. 17, pp. 914–923, 2019. View at Publisher · View at Google Scholar
  • Danial J. Armaghani, George D. Hatzigeorgiou, Chrysoula Karamani, Athanasia Skentou, Ioanna Zoumpoulaki, and Panagiotis G. Asteris, “Soft computing-based techniques for concrete beams shear strength,” Procedia Structural Integrity, vol. 17, pp. 924–933, 2019. View at Publisher · View at Google Scholar