Project Details
Projekt
Monitoring data driven life cycle management based on adaptive, AI-supported corrosion prediction for reinforced concrete structures under combined impacts
Applicants
Dr.-Ing. Thorsten Leusmann; Professor Dr.-Ing. Dirk Lowke; Professor Dr.-Ing. Henning Wessels
Subject Area
Construction Material Sciences, Chemistry, Building Physics
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 501798687
The aim of the proposed research project is a monitoring data-driven service life management system based on an adaptive, AI-supported corrosion prediction for chloride-exposured reinforced concrete structures under combined actions. The special focus will be on the extension to practice-relevant multiple exposure combinations, the integration of subsequently installed corrosion sensors and the derivation of recommendations for repair. The following specific objectives are being specifically addressed: - Expansion of the combined exposures to include cyclic loads, freeze-thaw cycles and intermittent moisture exposure and research into their effects on chloride transport in reinforced concrete components, - Integration of retrofitted corrosion sensors, such as the ring anode, - Corrosion monitoring of repaired reinforced concrete structures, - Extension of AI-supported corrosion prediction for multiple combinations of exposure and repaired reinforced concrete components with chloride exposure, taking into account existing data on chloride exposure from structural analyses, - Automated derivation of recommendations for action for the repair of chloride-loaded reinforced concrete structures, - Verification of the AI-supported service life management system on the Niebelungen Bridge in Worms
DFG Programme
Priority Programmes
