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

利用 AR 监测数据驱动的生命周期管理，基于人工智能支持的自适应腐蚀预测，对综合影响下的钢筋混凝土结构进行腐蚀预测

• 批准号: 501798687
• 负责人: Professor Dr.-Ing. Dirk Lowke
• 金额: --
• 依托单位: Institut für Rechnergestützte Modellierung im Bauingenieurwesen
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501798687?language=en
• 关键词: Monitoring; data; driven; life; cycle

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.

拟议的研究项目的目的是基于适应性，AI支持的腐蚀预测的监测数据驱动的服务生命管理系统，用于暴露于氯化物的钢筋混凝土结构。因此，将研究一个过程，该过程允许监视具有当前可用的测量原理的结构，并提供与结构数据库中数字双胞胎中链接相应的时间变化条件信息。重点是基于自适应AI的服务寿命预测，用于氯化物暴露和机械载荷的综合影响。物理知情的神经网络（PINN）将既用于解释局部和可能错误的传感器数据，也将用于适应3D预后模型的参数。监视和预测数据将在扩展的建筑信息模型（Digital Twin）中链接。因此，可以比较预测数据，构建几何形状和钢筋在组件中的位置，以识别问题领域并在增强现实（AR）的帮助下将问题区域可视化在真实组件上。因此，在结构检查中可以更容易地找到表面上尚不可见的缺陷。因此，服务生命管理系统可以实现对未来维修措施的优化和本地差异化的计划。