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
Optimal load and resistance factor design of geometrically nonlinear steel space frames via tabu search and genetic algorithm
Date
2008-01-01
Author
DEĞERTEKİN, SADIK ÖZGÜR
Saka, M. P.
HAYALİOĞLU, MEHMET SEDAT
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
258
views
0
downloads
Cite This
In this paper, algorithms are presented for the optimum design of geometrically nonlinear steel space frames using tabu search and genetic algorithm. Tabu search utilizes the features of short-term memory facility (tabu list) and aspiration criteria. Genetic algorithm employs reproduction, crossover and mutation operators. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) specification, maximum drift (lateral displacement) and interstorey drift constraints, size constraints for columns were imposed on frames. The algorithms were applied to the optimum design of three space frame structures. The designs obtained using tabu search were compared to those where genetic algorithm was considered. The comparisons showed that the former algorithm resulted in lighter structures.
Subject Keywords
Civil and Structural Engineering
URI
https://hdl.handle.net/11511/66871
Journal
ENGINEERING STRUCTURES
DOI
https://doi.org/10.1016/j.engstruct.2007.03.014
Collections
Department of Engineering Sciences, Article
Suggestions
OpenMETU
Core
Quantification and localisation of damage in beam-like structures by using artificial neural networks with experimental validation
Şahin, Melin (Elsevier BV, 2003-12-01)
This paper presents a damage detection algorithm using a combination of global (changes in natural frequencies) and local (curvature mode shapes) vibration-based analysis data as input in artificial neural networks (ANNs) for location and severity prediction of damage in beam-like structures. A finite element analysis tool has been used to obtain the dynamic characteristics of intact and damaged cantilever steel beams for the first three natural modes. Different damage scenarios have been introduced by redu...
Discrete Sizing of Steel Frames Using Adaptive Dimensional Search Algorithm
Hasançebi, Oğuzhan (Periodica Polytechnica Budapest University of Technology and Economics, 2019-01-01)
Adaptive dimensional search (ADS) algorithm is a recently proposed metaheuristic optimization technique for discrete structural optimization problems. In this study, discrete sizing optimization problem of steel frames is tackled using the ADS algorithm. An important feature of the algorithm is that it does not use any metaphor as an underlying principle for its implementation. Instead, the algorithm employs an efficient performance-oriented methodology at each iteration for convergence to the optimum or a ...
Predicting the shear strength of reinforced concrete beams using artificial neural networks
Mansour, MY; Dicleli, Murat; Lee, JY; Zhang, J (Elsevier BV, 2004-05-01)
The application of artificial neural networks (ANNs) to predict the ultimate shear strengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in this paper. An ANN model is built, trained and tested using the available test data of 176 RC beams collected from the technical literature. The data used in the ANN model are arranged in a format of nine input parameters that cover the cylinder concrete compressive strength, yield strength of the longitudinal and transverse reinforc...
Nonlinear Fiber Modeling of Steel-Concrete Partially Composite Beams with Channel Connectors
Ozturk, Alper; Baran, Eray; Tort, Cenk (Springer Science and Business Media LLC, 2019-05-01)
A simplified nonlinear fiber-based finite element model of steel-concrete partially composite beams utilizing channel type mechanical shear connectors is developed in OpenSees framework. The interaction between steel beam and concrete slab is accounted for by introducing nonlinear zero length elements and rigid links. The channel shear connector response used in numerical models is based on the previously obtained experimental response from pushout tests. Accuracy of the numerical models in predicting the r...
Linear static analysis of large structural models on pc clusters
Özmen, Semih; Toker, Kurç; Department of Civil Engineering (2009)
This research focuses on implementing and improving a parallel solution framework for the linear static analysis of large structural models on PC clusters. The framework consists of two separate programs where the first one is responsible from preparing data for the parallel solution that involves partitioning, workload balancing, and equation numbering. The second program is a fully parallel nite element program that utilizes substructure based solution approach with direct solvers. The first step of data...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. Ö. DEĞERTEKİN, M. P. Saka, and M. S. HAYALİOĞLU, “Optimal load and resistance factor design of geometrically nonlinear steel space frames via tabu search and genetic algorithm,”
ENGINEERING STRUCTURES
, pp. 197–205, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66871.