Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Efficient neural network-aided seismic life-cycle cost optimization of steel moment frames
Date
2024-09-01
Author
Gholizadeh, Saeed
Hasançebi, Oğuzhan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
60
views
0
downloads
Cite This
In this paper, a novel and efficient neural network-based methodology is proposed to achieve seismic total cost optimization of steel moment-resisting frames in a timely manner. The computational burden of an optimization process based on performing nonlinear time-history analysis is prohibitively high. To address this crucial issue, a new and efficient neural network model is proposed in this paper to accurately predict the nonlinear time history response of steel frames during the optimization process. In the proposed neural network model, an ensemble of parallel neural networks is used to provide excellent prediction accuracy. In addition, a new repairability constraint is proposed to check the seismic damage level of structures during the optimization process with the aid of the proposed neural network model. Moreover, an efficient metaheuristic algorithm is used to achieve the optimization task. Two numerical examples are illustrated to demonstrate the efficiency of the proposed methodology. The results show that the proposed neural network model outperforms the existing standard models in terms of prediction accuracy. Furthermore, it is shown that by using the proposed methodology, the optimal seismic total cost of steel frames increases by less than 2.5%, yet their seismic collapse capacity increases by at least 30%.
Subject Keywords
Fragility analysis
,
Life-cycle cost
,
Metaheuristic
,
Neural network
,
Nonlinear time history analysis
,
Performance-based design optimization
,
Steel moment frame
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85196784290&origin=inward
https://hdl.handle.net/11511/110313
Journal
Computers and Structures
DOI
https://doi.org/10.1016/j.compstruc.2024.107443
Collections
Department of Civil Engineering, Article
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. Gholizadeh and O. Hasançebi, “Efficient neural network-aided seismic life-cycle cost optimization of steel moment frames,”
Computers and Structures
, vol. 301, pp. 0–0, 2024, Accessed: 00, 2024. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85196784290&origin=inward.