Volume 14, Issue 2 (2-2024)
IJOCE 2024, 14(2): 275-293 |
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Jafar Z, Gholizadeh S. NEURAL NETWORK-BASED EVALUATION OF SEISMIC RESPONSE OF STEEL MOMENT FRAMES. IJOCE 2024; 14 (2) :275-293
URL: http://ijoce.iust.ac.ir/article-1-587-en.html
URL: http://ijoce.iust.ac.ir/article-1-587-en.html
1- Department of Civil Engineering, Urmia University, Urmia, Iran
Abstract: (4431 Views)
The main objective of this study is to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures at different seismic performance levels using feed-forward back-propagation (FFBP) neural network models. FFBP neural network models with varying numbers of hidden layer neurons (5, 10, 15, 20, and 50) were trained to predict the maximum inter-story drift ratios of 5- and 10-story steel MRF structures. The numerical simulations indicate that FFBP neural network models with ten hidden layer neurons better predict the inter-story drift ratios at seismic performance levels for both 5- and 10-story steel MRFs compared to other neural network models.
Keywords: seismic performance level, steel moment resisting frame, neural network, feed-forward back-propagation
Type of Study: Research |
Subject:
Applications
Received: 2024/04/10 | Accepted: 2024/05/20 | Published: 2024/05/27
Received: 2024/04/10 | Accepted: 2024/05/20 | Published: 2024/05/27
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