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Prediction of chloride permeability of concretes containing ground pozzolans by artificial neural networks

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Abstract

This research was to study the chloride penetration resistance of normal (W/B of 0.80, 0.62, 0.48) and high (W/B of 0.41, 0.35, 0.30) strength concretes containing ground pozzolans such as fly ash, bottom ash and rice husk ash using the rapid chloride penetration test and the immersion test methods. Furthermore, on the basis of this experimental data, an artificial neural network technique is carried out to derive an explicit artificial neural network formulation for the prediction of chloride permeability as a function of six input parameters: water to binder ratio, percent replacement, testing ages, pozzolans types, aggregate to cement ratio and the actual compressive strength. To verify the model, linear and non-linear regression equations are carried out and compared with the proposed artificial neural network prediction model. The results indicate that the incorporation of ground fly ash, ground bottom ash and ground rice husk ash substantially improve the workability and chloride permeability. The artificial neural network models have more accurate and precise prediction than linear and non-linear regression technique. Moreover, it is concluded that the artificial neural network models have a strong prediction capability of chloride penetration of concrete and can be easily expanded for the new additional database to re-train the network.

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Acknowledgments

The authors gratefully acknowledge the support of the faculty of engineering, Mahasarakham University and the substantial contributions of the members of concrete research laboratory (CRL).

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Authors and Affiliations

  1. Faculty of Engineering, Mahasarakham University, Mahasarakham, 44150, Thailand

    S. Inthata, W. Kowtanapanich & R. Cheerarot

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  1. S. Inthata
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  2. W. Kowtanapanich
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  3. R. Cheerarot
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Correspondence to R. Cheerarot.

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Inthata, S., Kowtanapanich, W. & Cheerarot, R. Prediction of chloride permeability of concretes containing ground pozzolans by artificial neural networks. Mater Struct 46, 1707–1721 (2013). https://doi.org/10.1617/s11527-012-0009-x

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  • DOI: https://doi.org/10.1617/s11527-012-0009-x

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