Machine learning applications for chloride ingress prediction in concrete: insights from recent literature
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Từ khóa:
short-term prediction
energy consumption
deep learning
convolutional neural network
metaheuristic optimization
time-series deep learning
machine learning
Tóm tắt
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.
Tài liệu tham khảo
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Nov 30, 2024
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Cách trích dẫn
Truong, Q.-C., và A.-T. N. Vu. “Machine Learning Applications for Chloride Ingress Prediction in Concrete: Insights from Recent Literature”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 22, số p.h 11B, Tháng Mười-Một 2024, tr 91-97, doi:10.31130/ud-jst.2024.528E.
