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Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.
This paper deals with the system identification of a mechanical structure supported by nonlinear springs subjected to an external load. If all mechanical parameters of the system were known, the displacement of the system subjected to this load could be easily calculated. However, the monitoring applications often deal with the inverse problem. The loads and displacements of the system are known and certain mechanical Parameters of the system are sought. The solution of such inverse problems can be difficult, especially when they have a nonlinear and multimodal character, which often makes them appear intractable at first sight. However, evolutionary computing can be applied to solve this inverse, nonlinear and multimodal problem. Sometimes a prior knowledge exists on certain system properties, which is difficult to implement into analytical or numerical solvers. This knowledge can play a decisive role in identifying the System properties and it can be easily included as a boundary condition when applying evolutionary algorithms.
This article discusses how and under what conditions the unknown spring resistances can be identified. The practical application of this procedure is exemplified here with the mechanical system of a pile foundation.