Digital twin as an intermediary between in-situ damage detection and global structural analysis
If structures are monitored from the beginning (Structural Health Monitoring, SHM), all information for damage prediction is available. This is generally not the case for existing structures. SHM on real structures aims to determine critical details based on deterministic or probabilistic considerations. Measurements are mainly carried out with strain gauges, displacement and acceleration sensors. Particularly with deterministic considerations, a lot of experience is required in the selection of measuring points. In addition, a certain degradation of the component must have occurred: cracks, corrosion and spalling, so that these can be detected during visual inspections (s. Figure 1).
The particular difficulty in developing suitable models to describe the condition of the structure is that the structures are highly variable in terms of building materials, construction methods, load-carrying structures, loading, age and condition and are subject to permanent change. Therefore, the information is scattered, uncertain and/or highly time-variable. However, the progress of damage to a critical state can take several decades. The information that accumulates on a building over the years has so far a decentralised character. As the connection has not been sufficient until now, many tasks are carried out redundantly, and knowledge already gained can be overlooked. The linking of the information could be done with the methods of Building Information Modelling (BIM), which has been introduced in recent years. So far, however, the special requirements of SHM have only been investigated in rudimentary form, and complete integration has not yet taken place. Questions regarding the handling of complex measurement systems that work on different scales have not yet been sufficiently investigated (s. Figure 2).
Therefore, the aim of this research project is to investigate fundamental questions on the digital linking of local in-situ damage detection methods and conventional SHM via Digital Twins (DT). Based on the investigations from aircraft construction, the concept of the DT principle established there, consisting of components with reduced order, is to be adapted and extended for bridge structures (s. Figure 3). The digital linking of SHM, in-situ DIC measurements and DT is carried out via "optimal classification trees" with "hyperplane splits" (OCT-H) to ensure interpretable machine learning. The particular challenge in the further development of the method for structural engineering is that the damage processes that lead to ageing and abrasion in structures are physical and chemical processes that must be recorded at different scales (material, component, structure level).
The research project focuses on the development of a cross-material and general method for linking in-situ measurements with DT. Other areas to be considered are optimal sensor placement, interpretable machine learning and online structural monitoring.