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
A New Design Approach for Rapid Evaluation of Structural Modifications Using Neural Networks
Date
2013-02-01
Author
Demirkan, O.
Olceroglu, E.
BAŞDOĞAN, FATMA İPEK
Özgüven, Hasan Nevzat
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
387
views
0
downloads
Cite This
Design optimization of structural systems is often iterative, time consuming and is limited by the knowledge of the designer. For that reason, a rapid design optimization scheme is desirable to avoid such problems. This paper presents and integrates two design methodologies for efficient conceptual design of structural systems involving computationally intensive analysis. The first design methodology used in this paper is structural modification technique (SMT). The SMT utilizes the frequency response functions (FRFs) of the original model for the reanalysis of the structure that is subjected to structural modification. The receptances of the original structure are coupled with the dynamic stiffness of the components that are added to or removed from the original structure to perform the structural modification. Then, the coupled matrices are used to calculate the mobility matrices of the modified structure in an efficient way. The second design methodology used in this paper is neural networks (NN). Once sufficient input and output relationships are obtained through SMT, a NN model is constructed to predict the FRF curves of the system for further analysis of the system performance while experimenting different design parameters. The input-output sets used for training the network are increased by applying an interpolation scheme to improve the accuracy of the NN model. The performance of the proposed method integrated through SMT and NN technique is demonstrated on a rectangular plate to observe the effect of the location and thickness of stiffeners on the frequency response of the structure. It is observed that both methods combined with the proposed interpolation scheme work very efficiently to predict the dynamic response of the structure when modifications are required to improve the system performance. [DOI: 10.1115/1.4023156]
Subject Keywords
Design optimization
,
Structural modification
,
Neural networks
,
System performance
URI
https://hdl.handle.net/11511/32787
Journal
JOURNAL OF MECHANICAL DESIGN
DOI
https://doi.org/10.1115/1.4023156
Collections
Department of Mechanical Engineering, Article
Suggestions
OpenMETU
Core
An exponential big bang-big crunch algorithm for discrete design optimization of steel frames
Hasançebi, Oğuzhan (2012-11-01)
In the present study an enhanced variant of the big bang-big crunch (BB-BC) technique, namely exponential BB-BC algorithm (EBB-BC) is developed for code based design optimization of steel frame structures. It is shown that the standard version of the BB-BC algorithm is sometimes unable to produce reasonable solutions to problems from discrete design optimization of steel frames. Hence, through investigating the shortcomings of BB-BC algorithm, it is aimed to reinforce the performance of the technique for th...
Sayısal Ortamda, Tasarımın Deneyimlenmesi İçin Arayüzlerin Geliştirilmesi: Bir Ön-Tasarım Parametresi Olarak Ses
Özgenel, Çağlar Fırat; Sorguç, Arzu (2011-01-01)
In architecture of our day, with the enhancement in technology, experiencing design decisions and / or changes by means of various computational technologies in computational medium has become an essential design tool. With the presence of the tools used in computational medium, changes in the architectural design process occurred and the usage of the tools drifted from simply virtual experience to optimization of the design by examining the design throughout the process. In the performance based design app...
Design Space Exploration of Initial Structural Design Alternatives via Artificial Neural Networks
Yetkin, Ozan; Sorguç, Arzu (2019-09-13)
Increasing implementation of digital tools within a design process generates exponentially growing data in each phase, and inevitably, decision making within a design space with increasing complexity will be a great challenge for the designers in the future. Hence, this research aimed to seek potentials of captured data within a design space and solution space of a truss design problem for proposing an initial novel approach to augment capabilities of digital tools by artificial intelligence where designers...
Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks
Kocamaz, Korhan; Binici, Barış; Tuncay, Kağan (2021-09-08)
Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensiv...
A reformulation of the ant colony optimization algorithm for large scale structural optimization
Hasançebi, Oğuzhan; Saka, M.p. (2011-01-01)
This study intends to improve performance of ant colony optimization (ACO) method for structural optimization problems particularly with many design variables or when design variables are chosen from large discrete sets. The algorithm developed with ACO method employs the so-called pheromone scaling approach to overcome entrapment of the search in a poor local optimum and thus to recover efficiency of the method for large-scale optimization problems. Besides, a new formulation is proposed for the local upda...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
O. Demirkan, E. Olceroglu, F. İ. BAŞDOĞAN, and H. N. Özgüven, “A New Design Approach for Rapid Evaluation of Structural Modifications Using Neural Networks,”
JOURNAL OF MECHANICAL DESIGN
, pp. 0–0, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32787.