Application of artificial neural networks to the evaluation of the ultimate strength of uniaxially compressed welded stiffened aluminium plates
A series of elastoplastic large-deflection finite element analyses is performed on stiffened aluminium plates with flat-bar stiffeners under in-plane longitudinal compression loads. Then, the closed-form ultimate compressive strength formula is derived for stiffened aluminium plates by regression analysis. Finally, artificial neural network methodology is applied to predict the ultimate strength of uniaxially compressed stiffened aluminium plates. It is found that artificial neural network models can produce a more accurate prediction of the ultimate strength of the stiffened aluminium plates than can the existing empirical formula.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14750902
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Supplemental Notes:
- Reprinted by permission of Sage Publications, Ltd.
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Authors:
- Zareei, Mohammad Reza
- Khedmati, Mohammad Reza
- Rigo, Philippe
- Publication Date: 2012-8
Language
- English
Media Info
- Media Type: Digital/other
- Features: Appendices; Figures; References; Tables;
- Pagination: pp 197-213
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Serial:
- Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment
- Volume: 226
- Issue Number: 3
- Publisher: Sage Publications, Incorporated
- ISSN: 1475-0902
- EISSN: 2041-3084
- Serial URL: http://pim.sagepub.com/
Subject/Index Terms
- TRT Terms: Aluminum alloys; Axial compression; Finite element method; Naval architecture; Neural networks; Shell plating; Stiffened plates; Strength of materials; Vehicle design
- Subject Areas: Design; Marine Transportation; Materials; Vehicles and Equipment; I34: Steels and Metals; I91: Vehicle Design and Safety;
Filing Info
- Accession Number: 01448726
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 9 2012 9:12AM