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Advances in Civil Engineering
Volume 2014 (2014), Article ID 684256, 14 pages
Research Article

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

Department of Science of Civil Engineering and Architecture, Technical University of Bari (Politecnico di Bari), Via Orabona 4, 70125 Bari, Italy

Received 27 February 2014; Revised 4 August 2014; Accepted 18 August 2014; Published 7 September 2014

Academic Editor: Hossein Moayedi

Copyright © 2014 Alessandra Fiore et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Despite their frequent occurrence in practice, only limited studies on the shear behavior 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 behavior 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.