7.6 Korrosion und Korrosionsschutz
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Der kritische, Korrosion auslösende Chloridgehalt für die Beurteilung der Korrosions-beständigkeit von Betonstahl in Geopolymerbetonen ist von großer Bedeutung für die Dauerhaftigkeitsprognose für bewehrte Elemente aus solchen Materialien. Es sind kaum experimentelle Daten in der Fachliteratur vorhanden und die vorliegenden Werte unterscheiden sich zwischen den Studien erheblich. In diesem Projekt wurde der kritische, Korrosion auslösende Chloridgehalt für Betonstahl (BSt 500) in flugaschebasierten (Ca-armen) Geopolymermörtel für verschiedene Beaufschlagungskombinationen bestimmt: Chloridbeaufschlagung mit 1 M NaCl-Lösung; Auslaugen in entionisiertem Wasser und anschließende Chloridbeaufschlagung im 1 M NaCl-Lösung; Auslaugen in entionisiertem Wasser, Carbonatisierung in Luft bei 20 °C und natürlicher CO2 Konzentration und anschließende Chloridbeaufschlagung mit 1 M NaCl-Lösung. Für Referenz-Zwecke wurde zusätzlich der Korrosion auslösende Chloridgehalt für eine Portland-Zement Mischung bei Auslagerung in 1 M NaCl-Lösung bestimmt.
The corrosion of steel in reinforced concrete structures is one of the main threats to their durability. Based on the scientific achievements of the past decades the knowledge about the deterioration mechanisms and possible repair strategies for corrosion induced damages have found their way into practice.
It is common sense, that a detailed assessment of the structure is the foundation for a successful repair measure. In addition to the “traditional” singular on-site-procedures such as measurement of concrete cover, carbonation depth, half-cell potentials and chloride contents the monitoring of corrosion related parameters has gained in importance over the past few years.
The advantages of a corrosion monitoring are obvious. In new buildings, structural elements which cannot be assessed after completion (e.g. tunnel segments), or members with electrically isolating coatings can be monitored by means of integrated sensors providing an insight into the development of crucial parameters such as electrochemical potentials, corrosion currents and the electrical resistivity of the concrete. A less known but very beneficial field of application is the use of corrosion monitoring as an integral part of a repair measure based on principles such as the cathodic protection of steel in concrete (CP) or increasing the electrical resistivity of the concrete (IR). By implementing a corrosion monitoring system, it is possible to survey the time dependent effect of the repair measure on the corrosion process which may lead to a confirmation of successful repair measure or to a modification of the repair strategy.
As the principle of cathodic protection for steel in concrete is a recognized repair measure today, the number of applications increases steadily and thus increasing the relevance for corrosion monitoring. Nevertheless, no standards or guidelines concerning the corrosion monitoring are available in Germany today, making it difficult to implement corrosion monitoring in common practice.
With this in mind an international task group formed to develop the specification B12 “Corrosion Monitoring of Reinforced and Prestressed Concrete Structures” published by the German Society for Non-Destructive Testing, DGZfP, spring 2018.
This paper will present the new specification B12 by highlighting the basic measurement principles and illustrating the potentials of corrosion monitoring for new and existing concrete structures by means of case studies.
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.