A new prediction model for the load capacity of castellated steel beams

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@Article{Gandomi20111096,

• author = "Amir Hossein Gandomi and Seyed Morteza Tabatabaei and Mohammad Hossein Moradian and Ata Radfar and Amir Hossein Alavi",
• title = "A new prediction model for the load capacity of castellated steel beams",
• journal = "Journal of Constructional Steel Research",
• volume = "67",
• number = "7",
• pages = "1096--1105",
• year = "2011",
• ISSN = "0143-974X",
• DOI = "doi:10.1016/j.jcsr.2011.01.014",
• URL = "http://www.sciencedirect.com/science/article/B6V3T-52BVR2R-1/2/9f40e5717143288037afed5176f8d52e",
• keywords = "genetic algorithms, genetic programming, Castellated beam, Failure load, Gene expression programming",
• abstract = "In this study, a robust variant of genetic programming, namely gene expression programming (GEP), is used to build a prediction model for the load capacity of castellated steel beams. The proposed model relates the load capacity to the geometrical and mechanical properties of the castellated beams. The model is developed based on a reliable database obtained from the literature. To verify the applicability of the derived model, it is employed to estimate the load capacity of parts of the test results that were not included in the modelling process. The external validation of the model was further verified using several statistical criteria recommended by researchers. A multiple least squares regression analysis is performed to benchmark the GEP-based model. A sensitivity analysis is further carried out to determine the contributions of the parameters affecting the load capacity. The results indicate that the proposed model is effectively capable of evaluating the failure load of the castellated beams. The GEP-based design equation is remarkably straightforward and useful for pre-design applications.",

}

Genetic Programming entries for A H Gandomi Seyed Morteza Tabatabaei Mohammad Hossein Moradian Ata Radfar A H Alavi

Citations