Automatic data-driven modeling and H2/H-infinity- norm-based dimension reduction of process-oriented and cooperative systems for SHM condition analysis with methods of system identification and machine learning on exposed structures

数字变革正在社会的各个领域引起深刻的变化。在BIM的融合中，工厂、建筑和基础设施的优化规划、执行和管理，以及结构健康监测（SHM）作为高效数据组织的核心要素的数字孪生体。本项目旨在实现基于H2/ h -无限范数和系统识别方法以及机器学习的自动化数据驱动建模方法。这使得在真正的孪生体（建筑）的使用寿命期间，作为数字孪生体的状态监测成为可能，这被纳入了SHM/BIM概念。基于面向过程的协同系统，特殊的物理可解释指标能够自动显示和定位结构变化。数值方法适用于随机多相关的仅输出的测量数据，并特别考虑和分类环境和操作条件。自动生成的系统参数化随机过程模型和滤波理论能够预测被测结构的未来损伤状态。这为公共当局提供了一套具有高经济效益的结构维护措施的预测性规划工具。

The digital change is causing profound changes in all areas of society. In the fusion of BIM, the optimized planning, execution and management of plants, buildings and infrastructures, with Structural Health Monitoring (SHM) a digital twin functions as a central element of an efficient data organization. The aim of this project is a method that realizes automated data-driven modeling based on the H2/H-infinite norm and methods of system identification coupled with machine learning. This enables a condition monitoring as a digital twin over the service life of the real twin, the building, which is incorporated into an SHM/BIM concept. Based on process-oriented cooperative systems, special physically interpretable indicators are able to automatically display and localize structural changes. The numerical method works with stochastic multi-correlated output-only measurement data, with special consideration and classification of environmental and operational conditions. The automatically generated parameterized stochastic process models of the system and filter theory enables a prediction of future damage states on the examined structure. This gives the public authority a set of tools for predictive planning of maintenance measures on structures with high economic benefits.