Machine learning applications for chloride ingress prediction in concrete: insights from recent literature
Chloride corrosion significantly impacts the durability of reinforced concrete (RC) structures. Traditional evaluation methods are time-consuming and expensive. Machine Learning (ML) offers a promising alternative, providing efficient and accurate predictions. This review explores recent ML advancem...
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| Format: | Article |
| Language: | English |
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The University of Danang
2024-11-01
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| Series: | Tạp chí Khoa học và Công nghệ |
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| Online Access: | https://jst-ud.vn/jst-ud/article/view/9545 |
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| _version_ | 1849721990323109888 |
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| author | Quynh-Chau Truong Anh-Thu Nguyen Vu |
| author_facet | Quynh-Chau Truong Anh-Thu Nguyen Vu |
| author_sort | Quynh-Chau Truong |
| collection | DOAJ |
| description | Chloride corrosion significantly impacts the durability of reinforced concrete (RC) structures. Traditional evaluation methods are time-consuming and expensive. Machine Learning (ML) offers a promising alternative, providing efficient and accurate predictions. This review explores recent ML advancements in assessing corrosion in RC structures. Various algorithms, such as Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Ensemble Learning, have shown potential in estimating corrosion processes, predicting material properties, and evaluating structural durability. Future research should focus on integrating ML with physical models to enhance robustness and reliability in service life prediction. This review summarizes current trends, challenges, and the future potential of ML in predicting chloride ingress and its impact on concrete durability. |
| format | Article |
| id | doaj-art-fb45dc6dcac7498281d12f0e0805dbec |
| institution | DOAJ |
| issn | 1859-1531 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | The University of Danang |
| record_format | Article |
| series | Tạp chí Khoa học và Công nghệ |
| spelling | doaj-art-fb45dc6dcac7498281d12f0e0805dbec2025-08-20T03:11:29ZengThe University of DanangTạp chí Khoa học và Công nghệ1859-15312024-11-01919710.31130/ud-jst.2024.528E9539Machine learning applications for chloride ingress prediction in concrete: insights from recent literatureQuynh-Chau Truong0Anh-Thu Nguyen Vu1The University of Danang - University of Science and Technology, VietnamThe University of Danang - University of Science and Technology, VietnamChloride corrosion significantly impacts the durability of reinforced concrete (RC) structures. Traditional evaluation methods are time-consuming and expensive. Machine Learning (ML) offers a promising alternative, providing efficient and accurate predictions. This review explores recent ML advancements in assessing corrosion in RC structures. Various algorithms, such as Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Ensemble Learning, have shown potential in estimating corrosion processes, predicting material properties, and evaluating structural durability. Future research should focus on integrating ML with physical models to enhance robustness and reliability in service life prediction. This review summarizes current trends, challenges, and the future potential of ML in predicting chloride ingress and its impact on concrete durability.https://jst-ud.vn/jst-ud/article/view/9545short-term predictionenergy consumptiondeep learningconvolutional neural networkmetaheuristic optimizationtime-series deep learningmachine learning |
| spellingShingle | Quynh-Chau Truong Anh-Thu Nguyen Vu Machine learning applications for chloride ingress prediction in concrete: insights from recent literature Tạp chí Khoa học và Công nghệ short-term prediction energy consumption deep learning convolutional neural network metaheuristic optimization time-series deep learning machine learning |
| title | Machine learning applications for chloride ingress prediction in concrete: insights from recent literature |
| title_full | Machine learning applications for chloride ingress prediction in concrete: insights from recent literature |
| title_fullStr | Machine learning applications for chloride ingress prediction in concrete: insights from recent literature |
| title_full_unstemmed | Machine learning applications for chloride ingress prediction in concrete: insights from recent literature |
| title_short | Machine learning applications for chloride ingress prediction in concrete: insights from recent literature |
| title_sort | machine learning applications for chloride ingress prediction in concrete insights from recent literature |
| topic | short-term prediction energy consumption deep learning convolutional neural network metaheuristic optimization time-series deep learning machine learning |
| url | https://jst-ud.vn/jst-ud/article/view/9545 |
| work_keys_str_mv | AT quynhchautruong machinelearningapplicationsforchlorideingresspredictioninconcreteinsightsfromrecentliterature AT anhthunguyenvu machinelearningapplicationsforchlorideingresspredictioninconcreteinsightsfromrecentliterature |