Monitoring data driven life cycle management with AR based on adaptive, AI-supported corrosion prediction for reinforced concrete structures under combined impacts

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The aim of the proposed research project is a monitoring data driven service life management system based on adaptive, AI-supported corrosion prediction for reinforced concrete structures exposed to chloride. Therefore, a procedure is to be researched that allows the monitoring of structures with currently available measuring principles and the provision of corresponding time-variant condition information for a link within a digital twin with a structure database. The focus is on an adaptive AI-based service life prediction for a combined impact of chloride exposure and mechanical loads. Physically Informed Neural Networks (PINN) will be used both for the interpretation of the local and possibly erroneous sensor data and for the adaptation of the parameters of the 3D prognosis model. The monitoring and forecast data will be linked in an extended building information model (digital twin). Prediction data, building geometry and the position of the steel reinforcement in the component can thus be compared in order to identify problem areas and visualise them on the real component with the help of augmented reality (AR). Defects that are not yet visible on the surface can thus be found and checked more easily during structural inspections. The service life management system thus enables optimised and locally differentiated planning of future repair measures.