Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/12512
Título: Engineering knowledge-based variance-reduction simulation and G-dominance for structural frame robust optimization
Autores/as: Greiner, D. 
Emperador, J. M. 
Galván González, Blas José 
Méndez, M. 
Winter, G. 
Clasificación UNESCO: 220502 Mecánica de medios continuos
120304 Inteligencia artificial
330532 Ingeniería de estructuras
Palabras clave: Structural optimization
Multiobjective optimization
Optimum design
Structural robust optimization
Fecha de publicación: 2013
Publicación seriada: Advances in Mechanical Engineering 
Resumen: This paper proposes the incorporation of engineering knowledge through both (a) advanced state-of-the-art preference handling decision-making tools integrated in multiobjective evolutionary algorithms and (b) engineering knowledge-based variance reduction simulation as enhancing tools for the robust optimum design of structural frames taking uncertainties into consideration in the design variables.The simultaneous minimization of the constrained weight (adding structuralweight and average distribution of constraint violations) on the one hand and the standard deviation of the distribution of constraint violation on the other are handled with multiobjective optimization-based evolutionary computation in two different multiobjective algorithms. The optimum design values of the deterministic structural problem in question are proposed as a reference point (the aspiration level) in reference-point-based evolutionary multiobjective algorithms (here g-dominance is used). Results including 𝑆-metric statistics in a structural frame test case with uncertain loads show considerable reductions in computational costs without harming the nondominated front quality, obtaining a design set that makes it possible to select minimum weight and maximum robustness optimum designs.
URI: http://hdl.handle.net/10553/12512
ISSN: 1687-8132
DOI: 10.1155/2013/680359
Fuente: Advances in Mechanical Engineering [ISSN 1687-8132], v. 2013 (680359), (Diciembre 2013)
Colección:Artículos
miniatura
Articulo Investigacion
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