Jafar Z, Gholizadeh S. NEURAL NETWORK-BASED EVALUATION OF SEISMIC RESPONSE OF STEEL MOMENT FRAMES. International Journal of Optimization in Civil Engineering 2024; 14 (2) :275-293
URL:
http://ijoce.iust.ac.ir/article-1-587-en.html
Abstract: (2006 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.
Type of Study:
Research |
Subject:
Applications Received: 2024/04/10 | Accepted: 2024/05/20 | Published: 2024/05/27