Prediction split tensile strength and water permeability of high strength concrete containing TiO2 nanoparticles by artificial neural network and genetic programming

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@Article{Nazari2011473,

• author = "Ali Nazari and Shadi Riahi",
• title = "Prediction split tensile strength and water permeability of high strength concrete containing {TiO2} nanoparticles by artificial neural network and genetic programming",
• journal = "Composites Part B: Engineering",
• volume = "42",
• number = "3",
• pages = "473--488",
• year = "2011",
• note = "See Retraction notice \cite{NAZARI:2021:CPBE}",
• keywords = "genetic algorithms, genetic programming, gene expression programming, Ceramic-matrix composites (CMCs), Strength, Computational modelling",
• ISSN = "1359-8368",
• DOI = "doi:10.1016/j.compositesb.2010.12.004",
• URL = "http://www.sciencedirect.com/science/article/B6TWK-51P9X22-2/2/c880a08e046f39a96d8b52a6df27266e",
• abstract = "In the present paper, two models based on artificial neural networks (ANN) and genetic programming (GEP) for predicting split tensile strength and percentage of water absorption of concretes containing TiO2 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT). According to these input parameters, in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing TiO2 nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing TiO2 nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network.",

}

Genetic Programming entries for Ali Nazari Shadi Riahi

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