Neural Network Prediction of Steel Corrosion Mechanism in Concrete Structure on Different Cement Concentrations
Main Article Content
Abstract
In the modern construction industry, reinforcement corrosion is an important phenomenon regarding the lifespan of any construction project. It is required to be tracked so that maintenance and repair actions can be initiated at the right time before any anticipated structural failure. In the recent past, tracking of reinforcement corrosion has been accomplished through a variety of scientific techniques, but the techniques usually involve some computational procedures. The fundamental purpose of this study is to determine the effectiveness of three different computational intelligent procedures for the prediction of corrosion in concrete structures. The study was accomplished through the method of emerging computational artificial intelligence techniques. In consequence, this study focuses on the application of artificial intelligence computational techniques of Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) for the determination of corrosion potential of embedded steel bar in concrete structures based on the required statistical analysis of the experimental dataset utilized. Results obtained revealed that the computational methods used are suitable tools for the prediction of the corrosion potential with a satisfactory level of accuracy. It was also discovered from the analysis of the result that the error margin is less than 10% of the prediction task with favourable outcomes. In furtherance, the computational capability of the DNN is greater than ANN and CNN, indicating its potential in the mathematical computational space.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Jacob Enewo Sanni, Federal Polytechnic Nasarawa, Nasarawa State, Nigeria.
Department of Civil Engineering Technology
Shaibu Ozovehe Abdullahi, Federal Polytechnic Nasarawa, Nasarawa State, Nigeria.
Department of Civil Engineering Technology
Ahmadi, S., & Khormali, A. (2024). Development of an RSM-based predictive model for evaluation of corrosion efficiency of ATMP in one molar HCl for carbon steel samples. Petroleum Science and Technology, 42(25), 4537-4555. DOI: https://doi.org/10.1080/10916466.2023.2253269
Alam, G., Ihsanullah, I., Naushad, M., & Sillanpää, M. (2022). Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects. Chemical Engineering Journal, 427, 130011. DOI: https://doi.org/10.1016/j.cej.2021.130011
Blackshaw, G. A., Forsdyke, J. C., & Lees, J. M. (2024). Effects of exposure sequence and GGBS cement replacement on performance of concrete subjected to carbonation and chloride ingress. Materials and Structures, 57(8), 178. DOI: https://doi.org/10.1617/s11527-024-02438-3
Chou, J. S., Ngo, N. T., & Chong, W. K. (2017). The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate. Engineering Applications of Artificial Intelligence, 65, 471-483. DOI: https://doi.org/10.1016/j.engappai.2016.09.008
Coelho, L. B., Zhang, D., Van Ingelgem, Y., Steckelmacher, D., Nowé, A., & Terryn, H. (2022). Reviewing machine learning of corrosion prediction in a data-oriented perspective. npj Materials Degradation, 6(1), 8. DOI: https://doi.org/10.1038/s41529-022-00218-4
Cusson, D., Lounis, Z., & Daigle, L. (2010). Benefits of internal curing on service life and life-cycle cost of high-performance concrete bridge decks–A case study. Cement and Concrete Composites, 32(5), 339-350. DOI: https://doi.org/10.1016/j.cemconcomp.2010.02.007
Folić, R., Zenunović, D., & Brujić, Z. (2024). Effects of carbonation and chloride ingress on the durability of concrete structures. Journal of the Serbian Chemical Society, (00), 30-30.
Gilan, S. S., Jovein, H. B., & Ramezanianpour, A. A. (2012). Hybrid support vector regression–Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin. Construction and Building Materials, 34, 321-329. DOI: https://doi.org/10.1016/j.conbuildmat.2012.02.038
Hodhod, O. A., & Ahmed, H. I. (2014). Modeling the corrosion initiation time of slag concrete using the artificial neural network. HBRC journal, 10(3), 231-234. DOI: https://doi.org/10.1016/j.hbrcj.2013.12.002
Ji, H., Wang, H., Chen, Q., Ma, X., & Cai, Y. (2024). Corrosion behavior prediction for hull steels under dynamic marine environments by jointly utilizing LSTM network and PSO-RF model. Ocean Engineering, 300, 117371. DOI: https://doi.org/10.1016/j.oceaneng.2024.117371
Karka, B. R., Bruno, B., Ngarmaïm, N., & Rimbarngaye, A. (2023). The Effects of Degradation Phenomena of the Steel-Concrete Interface in Reinforced Concrete Structures. Journal of Materials Science and Chemical Engineering, 11(3), 1-21. DOI: https://doi.org/10.4236/msce.2023.113001
Lv, Y. J., Wang, J. W., Wang, J., Xiong, C., Zou, L., Li, L., & Li, D. W. (2020). Steel corrosion prediction based on support vector machines. Chaos, Solitons & Fractals, 136, 109807. DOI: https://doi.org/10.1016/j.chaos.2020.109807
Mazer, W., Lima, M. G., & Medeiros-Junior, R. A. (2018). Fuzzy logic for estimating chloride diffusion in concrete. Proceedings of the Institution of Civil Engineers-Structures and Buildings, 171(7), 542-551. DOI: https://doi.org/10.1680/jstbu.16.00153
Michel, A., & Marcos-Meson, V. (2023). On the application of neural networks for service life prediction of steel fibre-reinforced concrete. Journal of Building Engineering, 76, 107286. DOI: https://doi.org/10.1016/j.jobe.2023.107286
Nguyen, T. T. T. (2024). Influence of top bar effect on corrosion behavior of reinforced concrete structure in case of chloride environment or concrete carbonation (Doctoral dissertation, Université de Toulouse; Ecole Nationale Supérieure de Génie Civil).
Nguyen, Q. H., Ly, H. B., Nguyen, T. A., Phan, V. H., Nguyen, L. K., & Tran, V. Q. (2021). Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams. Plos one, 16(4), e0247391. DOI: https://doi.org/10.1371/journal.pone.0247391
Phung, V. H., & Rhee, E. J. (2019). A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), 4500. DOI: https://doi.org/10.3390/app9214500
Scrivener, K. L., John, V. M., & Gartner, E. M. (2018). Eco-efficient cements: Potential economically viable solutions for a low-CO2 cement-based materials industry. Cement and concrete Research, 114, 2-26. DOI: https://doi.org/10.1016/j.cemconres.2018.03.015
Xia, J., Shen, J., Li, T., & Jin, W. L. (2022). Corrosion prediction models for steel bars in chloride-contaminated concrete: A review. Magazine of Concrete Research, 74(3), 123-142. DOI: https://doi.org/10.1680/jmacr.20.00106
Xiang, R. (2009). Prediction of concrete carbonation depth based on support vector regression. In 2009 Third International Symposium on Intelligent Information Technology Application (Vol. 3, pp. 172-175). IEEE. DOI: https://doi.org/10.1109/IITA.2009.469
Xue, H., Gao, Q., Zhao, Y., Li, X., Chen, J., Zhang, T., & Wang, F. (2024). A life prediction model for P110S steel in deep-well environments with H2S/CO2 coexistence based on multi-factor chemometric drive. Corrosion Science, 240, 112475. DOI: https://doi.org/10.1016/j.corsci.2024.112475
Wakjira, T. G., Abushanab, A., Alam, M. S., Alnahhal, W., & Plevris, V. (2024). Explainable machine learning-aided efficient prediction model and software tool for bond strength of concrete with corroded reinforcement. In Structures (Vol. 59, p. 105693). Elsevier. DOI: https://doi.org/10.1016/j.istruc.2023.105693
Wang, B., Liu, L., Cheng, X., Wu, W., Liu, C., Zhang, D., & Li, X. (2024). Advanced multi-image segmentation-based machine learning modeling strategy for corrosion prediction and rust layer performance evaluation of weathering steel. Corrosion Science, 237, 112334. DOI: https://doi.org/10.1016/j.corsci.2024.112334
Zhu, Y., Macdonald, D. D., Qiu, J., & Urquidi-Macdonald, M. (2021). Corrosion of rebar in concrete. Part III: Artificial Neural Network analysis of chloride threshold data. Corrosion Science, 185, 109438. DOI: https://doi.org/10.1016/j.corsci.2021.109438
Article Sidebar
