An evolutionary approach for modeling of shear strength of RC deep beams

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@Article{Gandomi:2014:MS,

• author = "Amir Hossein Gandomi and Gun Jin Yun and Amir Hossein Alavi",
• title = "An evolutionary approach for modeling of shear strength of RC deep beams",
• journal = "Materials and Structures",
• year = "2013",
• volume = "46",
• number = "12",
• pages = "2109--2119",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Gene expression programming, Shear strength, RC deep beams",
• publisher = "Springer Netherlands",
• ISSN = "1359-5997",
• DOI = "doi:10.1617/s11527-013-0039-z",
• language = "English",
• size = "11 pages",
• abstract = "In this study, a new variant of genetic programming, namely gene expression programming (GEP) is used to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. The model was developed using 214 experimental test results obtained from previously published papers. A comparative study was conducted between the results obtained by the proposed model and those of the American Concrete Institute (ACI) and Canadian Standard Association (CSA) models, as well as an Artificial Neural Network (ANN)-based model. A subsequent parametric analysis was carried out and the trends of the results were confirmed via some previous laboratory studies. The results indicate that the GEP model gives precise estimations of the shear strength of RC deep beams. The prediction performance of the model is significantly better than the ACI and CSA models and has a very good agreement with the ANN results. The derived design equation provides a valuable analysis tool accessible to practising engineers.",

}

Genetic Programming entries for A H Gandomi Gunjin Yun A H Alavi

Citations