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Computers and Concrete Volume 31, Number 4, April 2023 (Special Issue) pages 315-325 DOI: https://doi.org/10.12989/cac.2023.31.4.315 |
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Prediction of calcium leaching resistance of fly ash blended cement composites using artificial neural network |
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Yujin Lee, Seunghoon Seo, Ilhwan You, Tae Sup Yun and Goangseup Zi
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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. | ||
| Key Words | ||
| artificial intelligence; artificial neural networks; calcium leach; concrete durability; fly ash concrete; modeling | ||
| Address | ||
| Yujin Lee, Seunghoon Seo and Goangseup Zi: School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea Ilhwan You: Department of Rural Construction Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do, 54896, Republic of Korea Tae Sup Yun: School of Civil and Environmental Engineering, Yonsei Universitiy, Seoul, 03722, Republic of Korea | ||