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

Yujin Lee, Seunghoon Seo, Ilhwan You, Tae Sup Yun, Goangseup Zi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)315-325
Number of pages11
JournalComputers and Concrete
Volume31
Issue number4
DOIs
Publication statusPublished - 2023 Apr

Bibliographical note

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

Publisher Copyright:
Copyright © 2023 Techno-Press, Ltd.

Keywords

  • artificial intelligence
  • artificial neural networks
  • calcium leach
  • concrete durability
  • fly ash concrete
  • modeling

ASJC Scopus subject areas

  • Computational Mechanics

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