7.6 Korrosion und Korrosionsschutz
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- Corrosion (2)
- Anode (1)
- LIBS (1)
- Machine learning (1)
- Potential mapping (1)
- Zinc (1)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (2) (entfernen)
Galvanic corrosion protection by embedded zinc anodes is an accepted technique for the corrosion protection of reinforcing steel in concrete. Galvanic currents flow between the zinc anode and the steel reinforcement due to the potential difference that is in the range of a few hundred mV.
The ion distribution was studied on two steel reinforced concrete specimens admixed with 3 wt.% chloride/wt. cement and galvanically protected by a surface applied EZ-anode. On both specimens, a zinc anode was embedded and glued to the concrete surface by a geo-polymer-based chloride-free binder. At one specimen, the EZ-anode was operated for 2,5 years, the EZ-anode at the other specimen was not electrically connected to the reinforcement, this specimen serves as a reference. Both specimens have been stored under identical conditions. The ion distribution between the anode (EZ-ANODE) and cathode (steel reinforcement) was studied by laser-induced breakdown spectroscopy (LIBS) after 7 months, 12 months, and 2,5 years. Results of the LIBS studies on the specimen with activated EZ-anode after 7 months, 12 months, and 2,5 years and of the reference specimen after 2,5 years are reported. Results show that diffusion of ions contributes to the changes in the ion distribution but migration, especially of chlorides towards the EZ-anode is significant despite the weak electric field – several hundred millivolts - generated by the galvanic current. Results show that chloride ions accumulate near the zinc-anode as in water-insoluble zinc-hydroxy chlorides - Simonkollite.
This work presents machine learning-inspired data fusion approaches to improve the non-destructive testing of reinforced concrete. The principal effects that are used for data fusion are shown theoretically. Their effectiveness is tested in case studies carried out on largescale concrete specimens with built-in chloride-induced rebar corrosion. The dataset consists of half-cell potential mapping, Wenner resistivity, microwave moisture and ground penetrating radar measurements. Data fusion is based on the logistic Regression algorithm.
It learns an optimal linear decision boundary from multivariate labeled training data, to separate intact and defect areas. The training data are generated in an experiment that simulates the entire life cycle of chloride-exposed concrete building parts. The unique possibility to monitor the deterioration, and targeted corrosion initiation, allows data labeling.
The results exhibit an improved sensitivity of the data fusion with logistic regression compared to the best individual method half-cell potential.