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Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

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Abstract

Concrete carbonation is one of the main causes of corrosion of the reinforcement and consequently causing damage to the reinforced concrete structures. The progress of the carbonation front depends on many factors including mixture proportions and exposure conditions. Several carbonation prediction models including mathematical and analytical predictions are available. Most of these models, however, are based on simple regression equations and cannot predict or accurately reflect the various factors involved in concrete carbonation. The current published results in this issue are in conflict. In view of this, our research aims to apply an artificial neural network (ANN) approach for predicting the carbonation of fly-ash concrete taking into account the most influential parameters, including mixture proportions and exposure conditions. Six parameters were considered as inputs to the ANN model, covering, binder and fly-ash content, water-to-binder ratio, CO2 concentration, relative humidity, and time of exposure; one output is carbonation depth. The ANN model was prepared, trained, and tested with 300 datasets from experiments as well as past research. The performance of training, validation, and test sets shows a high correlation between the experimental and the ANN predicted values of the carbonation depth. In addition, the proposed prediction model was in good agreement with the experimental data in comparison with other model. This study concludes that the use of this model for numerical investigations on the parameters affecting the carbonation depth in fly-ash concrete is successful and provides scientific guidance for durability design.

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Authors and Affiliations

  1. Geomaterials Laboratory, Department of Civil Engineering, University of Hassiba Benbouali, Chlef, BP 151, 02000, Chlef, Algeria

    Yasmina Kellouche, Bakhta Boukhatem & Mohamed Ghrici

  2. Department of Civil Engineering, University of Sherbrooke, 2500 boul. de l’université de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada

    Bakhta Boukhatem & Arezki Tagnit-Hamou

Authors
  1. Yasmina Kellouche
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  2. Bakhta Boukhatem
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  3. Mohamed Ghrici
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  4. Arezki Tagnit-Hamou
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Correspondence to Bakhta Boukhatem.

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Appendix: Data sources and variable construction

Appendix: Data sources and variable construction

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Table 8 Data sources
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Kellouche, Y., Boukhatem, B., Ghrici, M. et al. Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network. Neural Comput & Applic 31 (Suppl 2), 969–988 (2019). https://doi.org/10.1007/s00521-017-3052-2

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