Volume 16, Issue 2 (4-2026)
IJOCE 2026, 16(2): 165-184 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hadi Ajli A S, Gholizadeh S. PREDICTION OF NONLINEAR INTER-STORY DRIFTS IN STEEL MOMENT FRAMES UNDER SEISMIC LOADING USING CASCADE FORWARD NEURAL NETWORKS. IJOCE 2026; 16 (2) :165-184
URL: http://ijoce.iust.ac.ir/article-1-665-en.html
URL: http://ijoce.iust.ac.ir/article-1-665-en.html
1- Department of Civil Engineering, Urmia University, Urmia, Iran, Sero Rd., Nazlu Campus, P.O. Box 165, Urmia, Iran
Abstract: (27 Views)
This paper aims to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures under seismic loading, corresponding to different performance levels, using cascade-forward back-propagation (CFBP) neural network models. To this end, CFBP networks with varying numbers of hidden layer neurons are trained on nonlinear time-history analysis results of 6- and 12-story planar steel MRFs subjected to a suite of earthquake ground motions. The predictive performance of the trained models is systematically compared. Numerical results demonstrate that CFBP networks with 15 neurons in the hidden layer consistently outperform other network architectures, yielding more accurate predictions of the maximum inter-story drift ratios at each seismic performance level for both frame heights. These findings highlight the potential of moderately sized CFBP networks as efficient surrogates for nonlinear dynamic analysis in performance-based seismic assessment.
Keywords: nonlinear time history analysis, steel moment resisting frame, performance level, cascade-forward back-propagation, neural network
Type of Study: Research |
Subject:
Optimal design
Received: 2026/01/26 | Accepted: 2026/03/29 | Published: 2026/04/2
Received: 2026/01/26 | Accepted: 2026/03/29 | Published: 2026/04/2
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
