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

拟议研究项目的目的是建立一个监测数据驱动的使用寿命管理系统，该系统基于自适应的人工智能支持的腐蚀预测，用于暴露于氯化物的钢筋混凝土结构。因此，需要研究一种程序，该程序允许使用当前可用的测量原理对结构进行监测，并为具有结构数据库的数字孪生中的链接提供相应的时变条件信息。重点是基于人工智能的自适应使用寿命预测，以应对氯化物暴露和机械负荷的综合影响。物理信息神经网络（PINN）将用于解释局部和可能错误的传感器数据，并用于适应3D预后模型的参数。监测和预测数据将在一个扩展的建筑信息模型（数字孪生）中链接。因此，可以比较预测数据、建筑几何形状和钢筋在组件中的位置，以便识别问题区域，并在增强现实（AR）的帮助下将其可视化到真实组件上。因此，在结构检查期间，可以更容易地发现和检查表面上尚不可见的缺陷。因此，使用寿命管理系统使未来维修措施的优化和局部差异化规划成为可能。

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.