Transfer learning enables prediction of steel corrosion in concrete under natural environments
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OpenAlex topics: Concrete Corrosion and Durability · Infrastructure Maintenance and Monitoring · Non-Destructive Testing Techniques
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References (42)
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1. Corrosion of steel rebar in concrete induced by chloride ions under natural environments10.1016/j.conbuildmat.2023.130504 ↗
2. Challenges and opportunities in corrosion of steel in concrete10.1617/s11527-017-1131-6 ↗
3. Bertolini · 2013
4. Corrosion rates of steel in concrete10.1016/0008-8846(86)90051-7 ↗
5. Prediction of corrosion rate in reinforced concrete structures – a critical review and preliminary results10.1002/maco.201106282 ↗
6. Prediction of corrosion rate in RC structures - a critical review
7. Corrosion prediction models for steel bars in chloride-contaminated concrete: a review10.1680/jmacr.20.00106 ↗
8. Empirical model of corrosion rate for steel reinforced concrete structures in chloride-laden environments10.1177/1369433218783313 ↗
9. Experimental study and suggested mathematical model for chloride-induced reinforcement corrosion rate10.1016/j.istruc.2021.08.099 ↗
10. Numerical simulation of steel corrosion in chloride contaminated concrete10.1016/j.conbuildmat.2019.116745 ↗
11. Non-uniform rust expansion for chloride-induced pitting corrosion in RC structures10.1016/j.conbuildmat.2013.10.042 ↗
12. Machine learning prediction of corrosion rate of steel in carbonated cementitious mortars10.1016/j.cemconcomp.2023.105256 ↗
13. Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm10.1007/s00521-014-1645-6 ↗
14. Practical model for predicting corrosion rate of steel reinforcement in concrete structures10.1016/j.conbuildmat.2013.12.046 ↗
15. Machine learning in concrete science: applications, challenges, and best practices10.1038/s41524-022-00810-x ↗
16. Reviewing machine learning of corrosion prediction in a data-oriented perspective
17. Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning10.1016/j.corsci.2020.108697 ↗
18. Measurement of reinforcement corrosion in concrete adopting ultrasonic tests and artificial neural network10.1016/j.conbuildmat.2018.05.124 ↗
19. A survey on transfer learning10.1109/TKDE.2009.191 ↗
20. A comprehensive survey on transfer learning10.1109/JPROC.2020.3004555 ↗
21. Structural health monitoring using extremely compressed data through deep learning10.1111/mice.12517 ↗
22. Transfer (machine) learning approaches coupled with target data augmentation to predict the mechanical properties of concrete10.1016/j.mlwa.2022.100271 ↗
23. Evaluation of transfer learning models for predicting the lateral strength of reinforced concrete columns10.1016/j.engstruct.2022.114579 ↗
24. A transfer-learning approach for corrosion prediction in pipeline infrastructures10.1007/s10489-021-02771-y ↗
25. Utilization of silica fume in concrete: review of hardened properties10.1016/j.resconrec.2011.06.012 ↗
26. Feature selection in machine learning: a new perspective10.1016/j.neucom.2017.11.077 ↗
27. Rice · 2006
28. Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model10.1016/j.corsci.2020.109084 ↗
29. Flood stage forecasting with support vector machines10.1111/j.1752-1688.2002.tb01544.x ↗
30. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: new insights from statistical analysis and machine learning methods10.1016/j.cemconres.2018.09.006 ↗
31. XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring10.1016/j.autcon.2020.103155 ↗
32. Bonaccorso · 2017
33. Mitchell · 1997
34. A unified approach to interpreting model predictions
35. Characterisation of chloride transport and reinforcement corrosion in concrete under cyclic wetting and drying by electrical resistivity10.1016/S0958-9465(01)00074-9 ↗
36. Comparative studies of experimental and numerical techniques in measurement of corrosion rate and time-to-corrosion-initiation of rebar in concrete in marine environments10.1016/j.cemconcomp.2013.11.001 ↗
37. Corrosion of reinforcing steel evaluated by means of concrete resistivity measurements10.1016/S0010-938X(01)00033-6 ↗
38. Prediction of concrete porosity using machine learning10.1016/j.rineng.2022.100794 ↗
39. An adaptive approach for ice detection in wind turbine with inductive transfer learning10.1109/ACCESS.2019.2926575 ↗
40. KNN model-based approach in classification
41. A survey on knowledge transfer for manufacturing data analytics10.1016/j.compind.2018.07.001 ↗
42. A survey of feature selection and feature extraction techniques in machine learning