| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Steels in Extreme Environments
|
| Presentation Title |
The Dynamic Behavior of Rebar Corrosion: Coupled Point Defect Theory, Machine Learning and Experimental Validation |
| Author(s) |
Yakun Zhu, Digby D Macdonald |
| On-Site Speaker (Planned) |
Yakun Zhu |
| Abstract Scope |
Rebar corrosion in reinforced concrete is triggered primarily by the presence of chloride. A metric known as chloride threshold (CT) has been developed to describe the susceptibility of steel to chloride-induced passivity breakdown. We have established a rich database of CT and its associated primary and secondary influencing factors, in which each vector includes about 20 variables. Statistical analyses reveal that CT is lognormally distributed whereas potential parameters are normally distributed. We developed a theoretical basis in terms of point defect, so that CT can be effectively related to the properties of steel in passive state and to its susceptibility to chloride-induced passivity breakdown. We also demonstrated that it is possible to calculate CT in pure, empirical manner using machine learning techniques, in accordance with theoretical prediction, through artificial neural network which was trained based on the CT database. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Iron and Steel, Machine Learning, Other |