Neural networks and cold-formed steel design

E.M.A. El-Kassas; R.I. Mackie; A.I. El-Sheikh

doi:10.4203/ccp.64.1.6

Neural networks and cold-formed steel design

E.M.A. El-Kassas, R.I. Mackie, A.I. El-Sheikh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The application of neural networks to cold-formed steel design is considered. Cold-formed steel design is more complex than hot-rolled steel design because of the large number of different section profiles, and traditional computing tools are not well-suited for dealing with this. The flexibility of neural networks makes them a suitable alternative. The paper describes the use of neural networks to predict the failure load of cold-formed sections, and the results are in good agreement with results from design codes.

Original language	English
Title of host publication	Computational engineering using metaphors from nature
Editors	B.H.V. Topping
Place of Publication	Edinburgh
Publisher	Civil-Comp Press
Pages	37-43
Number of pages	7
Volume	64
ISBN (Print)	0948749660
DOIs	https://doi.org/10.4203/ccp.64.1.6
Publication status	Published - 2000
Event	5th International Conference on Computational Structures Technology/2nd International Conference on Engineering Computational Technology - LEUVEN, Belgium Duration: 6 Sept 2000 → 8 Sept 2000 http://www.civil-comp.com/conf/prog2000.htm

Conference

Conference	5th International Conference on Computational Structures Technology/2nd International Conference on Engineering Computational Technology
Country/Territory	Belgium
City	LEUVEN
Period	6/09/00 → 8/09/00
Internet address	http://www.civil-comp.com/conf/prog2000.htm

Access to Document

10.4203/ccp.64.1.6

Cite this

@inproceedings{9ed7bc1aae674fb19397920c086bfb90,

title = "Neural networks and cold-formed steel design",

abstract = "The application of neural networks to cold-formed steel design is considered. Cold-formed steel design is more complex than hot-rolled steel design because of the large number of different section profiles, and traditional computing tools are not well-suited for dealing with this. The flexibility of neural networks makes them a suitable alternative. The paper describes the use of neural networks to predict the failure load of cold-formed sections, and the results are in good agreement with results from design codes.",

author = "E.M.A. El-Kassas and R.I. Mackie and A.I. El-Sheikh",

year = "2000",

doi = "10.4203/ccp.64.1.6",

language = "English",

isbn = "0948749660",

volume = "64",

pages = "37--43",

editor = "B.H.V. Topping",

booktitle = "Computational engineering using metaphors from nature",

publisher = "Civil-Comp Press",

note = "5th International Conference on Computational Structures Technology/2nd International Conference on Engineering Computational Technology ; Conference date: 06-09-2000 Through 08-09-2000",

url = "http://www.civil-comp.com/conf/prog2000.htm",

}

El-Kassas, EMA, Mackie, RI & El-Sheikh, AI 2000, Neural networks and cold-formed steel design. in BHV Topping (ed.), Computational engineering using metaphors from nature. vol. 64, Civil-Comp Press, Edinburgh, pp. 37-43, 5th International Conference on Computational Structures Technology/2nd International Conference on Engineering Computational Technology, LEUVEN, Belgium, 6/09/00. https://doi.org/10.4203/ccp.64.1.6

TY - GEN

T1 - Neural networks and cold-formed steel design

AU - El-Kassas, E.M.A.

AU - Mackie, R.I.

AU - El-Sheikh, A.I.

PY - 2000

Y1 - 2000

N2 - The application of neural networks to cold-formed steel design is considered. Cold-formed steel design is more complex than hot-rolled steel design because of the large number of different section profiles, and traditional computing tools are not well-suited for dealing with this. The flexibility of neural networks makes them a suitable alternative. The paper describes the use of neural networks to predict the failure load of cold-formed sections, and the results are in good agreement with results from design codes.

AB - The application of neural networks to cold-formed steel design is considered. Cold-formed steel design is more complex than hot-rolled steel design because of the large number of different section profiles, and traditional computing tools are not well-suited for dealing with this. The flexibility of neural networks makes them a suitable alternative. The paper describes the use of neural networks to predict the failure load of cold-formed sections, and the results are in good agreement with results from design codes.

U2 - 10.4203/ccp.64.1.6

DO - 10.4203/ccp.64.1.6

M3 - Conference contribution

SN - 0948749660

VL - 64

SP - 37

EP - 43

BT - Computational engineering using metaphors from nature

A2 - Topping, B.H.V.

PB - Civil-Comp Press

CY - Edinburgh

T2 - 5th International Conference on Computational Structures Technology/2nd International Conference on Engineering Computational Technology

Y2 - 6 September 2000 through 8 September 2000

ER -

Neural networks and cold-formed steel design

Abstract

Conference

Access to Document

Fingerprint

Cite this