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

数字化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，建筑物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元素。该项目的目标是实现基于H2/H-无限范数的自动数据驱动建模方法以及与机器学习相结合的系统识别方法。这使得在真实的双胞胎（建筑物）的使用寿命期间，可以作为数字双胞胎进行状态监测，并将其纳入SHM/BIM概念。基于面向过程的协作系统，特殊的物理可解释的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测量数据，并对环境和操作条件进行特殊考虑和分类。自动生成的参数化随机过程模型的系统和过滤理论，使未来的损伤状态的预测上检查的结构。这为公共当局提供了一套工具，用于预测规划具有高经济效益的结构的维护措施。

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.