Volume 14, Issue 2 (2-2024)                   2024, 14(2): 275-293 | Back to browse issues page

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NEURAL NETWORK-BASED EVALUATION OF SEISMIC RESPONSE OF STEEL MOMENT FRAMES
Z.H.F. Jafar , S. Gholizadeh * 1
Abstract:   (2012 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
Full-Text [PDF 1656 kb]   (259 Downloads)    
Type of Study: Research | Subject: Applications
Received: 2024/04/10 | Accepted: 2024/05/20 | Published: 2024/05/27
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