Volume 14, Issue 4 (10-2024)
IJOCE 2024, 14(4): 629-645 |
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Hassan Radhi Alhilali A, Gholizadeh S, Tariverdilo S. SEISMIC CONFIDENCE LEVELS AND COLLAPSE CAPACITY ASSESSMENT OF STEEL MOMENT RESISTING FRAMES USING NEURAL NETWORKS. IJOCE 2024; 14 (4) :629-645
URL: http://ijoce.iust.ac.ir/article-1-612-en.html
URL: http://ijoce.iust.ac.ir/article-1-612-en.html
1- Department of Civil Engineering, Urmia University, Urmia, Iran
Abstract: (618 Views)
This paper employs neural network models to assess the seismic confidence levels at various performance levels, as well as the seismic collapse capacity of steel moment-resisting frame structures. Two types of shallow neural network models including back-propagation (BP) and radial basis (RB) models are utilized to evaluate the seismic responses. Both neural network models consist of a single hidden layer with a different number of neurons. The prediction accuracy of the trained neural network models is compared using two illustrative examples of 6- and 12-story steel moment-resisting frames. The obtained numerical results indicate that the BP model outperforms the RB model in predicting seismic responses.
Keywords: seismic life cycle cost, performance-based design, nonlinear response history analysis, steel moment resisting frame
Type of Study: Research |
Subject:
Applications
Received: 2024/11/11 | Accepted: 2024/12/25 | Published: 2024/10/16
Received: 2024/11/11 | Accepted: 2024/12/25 | Published: 2024/10/16
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