Evolutionary Modeling to Evaluate the Shear Behavior of Circular Reinforced Concrete Columns

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@Article{Fiore:2014:ace,

• author = "Alessandra Fiore and Giuseppe Carlo Marano and Daniele Laucelli and Pietro Monaco",
• title = "Evolutionary Modeling to Evaluate the Shear Behavior of Circular Reinforced Concrete Columns",
• journal = "Advances in Civil Engineering",
• year = "2014",
• volume = "2014",
• pages = "Article ID 684256",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://downloads.hindawi.com/journals/ace/2014/684256.pdf",
• DOI = "doi:10.1155/2014/684256.",
• size = "15 pages",
• abstract = "Despite their frequent occurrence in practice, only limited studies on the shear behaviour of reinforced concrete (RC) circular members are available in the literature. Such studies are based on poor assumptions about the physical model, often resulting in being too conservative, as well as technical codes that essentially propose empirical conversion rules. On this topic in this paper, an evolutionary approach named EPR is used to create a structured polynomial model for predicting the shear strength of circular sections. The adopted technique is an evolutionary data mining methodology that generates a transparent and structured representation of the behaviour of a system directly from experimental data. In this study experimental data of 61 RC circular columns, as reported in the technical literature, are used to develop the EPR models. As final result, physically consistent shear strength models for circular columns are obtained, to be used in different design situations. The proposed formulations are compared with models available from building codes and literature expressions, showing that EPR technique is capable of capturing and predicting the shear behavior of RC circular elements with very high accuracy. A parametric study is also carried out to evaluate the physical consistency of the proposed models.",
• notes = "Department of Science of Civil Engineering and Architecture, Technical University of Bari (Politecnico di Bari), Via Orabona 4, 70125 Bari, Italy",

}

Genetic Programming entries for Alessandra Fiore Giuseppe Carlo Marano Daniele B Laucelli Pietro Monaco

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