Computers and Concrete

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Prediction of calcium leaching resistance of fly ash blended cement composites using artificial neural network

  • Yujin Lee (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Seunghoon Seo (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Ilhwan You (Department of Rural Construction Engineering, Jeonbuk National University) ;
  • Tae Sup Yun (School of Civil and Environmental Engineering, Yonsei Universitiy) ;
  • Goangseup Zi (School of Civil, Environmental and Architectural Engineering, Korea University)
  • Received : 2022.12.24
  • Accepted : 2023.02.18
  • Published : 2023.04.25
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Abstract

Calcium leaching is one of the main deterioration factors in concrete structures contact with water, such as dams, water treatment structures, and radioactive waste structures. It causes a porous microstructure and may be coupled with various harmful factors resulting in mechanical degradation of concrete. Several numerical modeling studies focused on the calcium leaching depth prediction. However, these required a lot of cost and time for many experiments and analyses. This study presents an artificial neural network (ANN) approach to predict the leaching depth quickly and accurately. Totally 132 experimental data are collected for model training and validation. An optimal ANN model was proposed by ANN topology. Results indicate that the model can be applied to estimate the calcium leaching depth, showing the determination coefficient of 0.91. It might be used as a simulation tool for engineering problems focused on durability.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A5A1032433).

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