An empirical model for shear capacity of RC deep beams using genetic-simulated annealing

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@Article{Gandomi:2013:ACME,

• author = "A. H. Gandomi and A. H. Alavi and D. Mohammadzadeh Shadmehri and M. G. Sahab",
• title = "An empirical model for shear capacity of {RC} deep beams using genetic-simulated annealing",
• journal = "Archives of Civil and Mechanical Engineering",
• year = "2013",
• volume = "13",
• number = "3",
• pages = "354--369",
• keywords = "genetic algorithms, genetic programming, Shear capacity, RC deep beam, Genetic-simulated annealing, Empirical formula",
• ISSN = "1644-9665",
• DOI = "doi:10.1016/j.acme.2013.02.007",
• URL = "http://www.sciencedirect.com/science/article/pii/S1644966513000319",
• size = "16 pages",
• abstract = "This paper presents an empirical model to predict the shear strength of RC deep beams. A hybrid search algorithm coupling genetic programming (GP) and simulated annealing (SA), called genetic simulated annealing (GSA), was used to develop mathematical relationship between the experimental data. Using this algorithm, a constitutive relationship was obtained to make pertinent the shear strength of deep beams to nine mechanical and geometrical parameters. The model was developed using an experimental database acquired from the literature. The results indicate that the proposed empirical model is properly capable of evaluating the shear strength of deep beams. The validity of the proposed model was examined by comparing its results with those obtained from American Concrete Institute (ACI) and Canadian Standard Association (CSA) codes. The derived equation is notably simple and includes several effective parameters.",

}

Genetic Programming entries for A H Gandomi A H Alavi D Mohammadzadeh Shadmehri Mohammad Ghasem Sahab

Citations