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Modeling the mechanical properties of rubberized concretes by neural network and genetic programming

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Abstract

Using the discarded tire rubber as the concrete aggregate may be a solution to disposal of this waste material. However, presence of the rubber aggregates in the concrete mixture decrease the mechanical properties of such concretes depending mainly on the type and the content of the rubber used. In this paper, neural network (NN) and genetic programming (GEP) based explicit models are proposed for the prediction of mechanical properties of rubberized concretes. Data used in both training and testing of NN and GEP models were obtained from an experimental study containing a total of 70 rubberized concretes. The models were constructed using eight design variables and one response as the inputs and output, respectively. Compressive strength, splitting tensile strength, and static elastic modulus of the concretes were employed as the outputs of the models developed in this study. It is found that both NN and GEP provided high prediction capability with certain accuracy. The proposed formulations also showed perfect agreement with the experimental study, thus leading to beneficial and practical estimation of the mechanical properties of the rubberized concretes.

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Authors and Affiliations

  1. Department of Civil Engineering, Gaziantep University, 27310, Gaziantep, Turkey

    Mehmet Gesoğlu & Erhan Güneyisi

  2. Department of Civil Engineering, Bogazici University, 34342, Istanbul, Turkey

    Turan Özturan

  3. Vocational High School, Kilis 7 Aralık University, 79000, Kilis, Turkey

    Erdoğan Özbay

Authors
  1. Mehmet Gesoğlu
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  2. Erhan Güneyisi
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  3. Turan Özturan
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  4. Erdoğan Özbay
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Correspondence to Mehmet Gesoğlu.

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Gesoğlu, M., Güneyisi, E., Özturan, T. et al. Modeling the mechanical properties of rubberized concretes by neural network and genetic programming. Mater Struct 43, 31–45 (2010). https://doi.org/10.1617/s11527-009-9468-0

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  • DOI: https://doi.org/10.1617/s11527-009-9468-0

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