This paper presents two plate strength formulations
applicable to metals with nonlinear stress-strain
curves, such as aluminium and stainless steel alloys,
obtained by soft computing techniques, namely Neural
Networks (ANN) and Genetic Programming (GP). The
proposed soft computing formulations are based on
well-defined FE results available in the literature.
The proposed formulations enable determination of the
buckling strength of rectangular plates in terms of
RambergOsgood parameters. The strength curves obtained
by the proposed soft computing formulations show
perfect agreement with FE results. The formulations are
later compared with related codes and results are found
to be quite satisfactory.
%0 Journal Article
%1 Cevik:2007:ES
%A Cevik, Abdulkadir
%A Guzelbey, Ibrahim H.
%D 2007
%J Engineering Structures
%K Buckling, Neural Plates Soft algorithms, computing, genetic networks, programming,
%N 3
%P 383--394
%R doi:10.1016/j.engstruct.2006.05.005
%T A soft computing based approach for the prediction of
ultimate strength of metal plates in compression
%V 29
%X This paper presents two plate strength formulations
applicable to metals with nonlinear stress-strain
curves, such as aluminium and stainless steel alloys,
obtained by soft computing techniques, namely Neural
Networks (ANN) and Genetic Programming (GP). The
proposed soft computing formulations are based on
well-defined FE results available in the literature.
The proposed formulations enable determination of the
buckling strength of rectangular plates in terms of
RambergOsgood parameters. The strength curves obtained
by the proposed soft computing formulations show
perfect agreement with FE results. The formulations are
later compared with related codes and results are found
to be quite satisfactory.
@article{Cevik:2007:ES,
abstract = {This paper presents two plate strength formulations
applicable to metals with nonlinear stress-strain
curves, such as aluminium and stainless steel alloys,
obtained by soft computing techniques, namely Neural
Networks (ANN) and Genetic Programming (GP). The
proposed soft computing formulations are based on
well-defined FE results available in the literature.
The proposed formulations enable determination of the
buckling strength of rectangular plates in terms of
RambergOsgood parameters. The strength curves obtained
by the proposed soft computing formulations show
perfect agreement with FE results. The formulations are
later compared with related codes and results are found
to be quite satisfactory.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Cevik, Abdulkadir and Guzelbey, Ibrahim H.},
biburl = {https://www.bibsonomy.org/bibtex/221d6282c43985216cad23d525d738d83/brazovayeye},
doi = {doi:10.1016/j.engstruct.2006.05.005},
interhash = {ac78842c0fa2e644c533e1e4362ec0a8},
intrahash = {21d6282c43985216cad23d525d738d83},
journal = {Engineering Structures},
keywords = {Buckling, Neural Plates Soft algorithms, computing, genetic networks, programming,},
month = {March},
number = 3,
pages = {383--394},
timestamp = {2008-06-19T17:37:27.000+0200},
title = {A soft computing based approach for the prediction of
ultimate strength of metal plates in compression},
volume = 29,
year = 2007
}