Die Brückeninfrastruktur in Deutschland und Europa steht aufgrund steigender Verkehrslasten und alternder Bauwerke vor erheblichen Herausforderungen. Das DFG-Schwerpunktprogramm 2388 „Hundert plus – Verlängerung der Lebensdauer komplexer Baustrukturen durch intelligente Digitalisierung“ (SPP100+) strebt an, durch digitale Innovationen und prädiktive Instandhaltungsstrategien die Nutzungsdauer bestehender Brückenbauwerke zu verlängern. Der vorliegende Beitrag fokussiert sich auf das SPP100+ zugehörige Cluster „Monitoring und Simulation“, das sieben Teilprojekte umfasst. Die Projekte entwickeln fortschrittliche Methoden zur Überwachung und Zustandsbewertung von Brücken mittels Digitaler Zwillinge, hochauflösender Sensortechnik und numerischer Simulationen. Innovative Ansätze wie nichtlineare Modellanpassungen, stochastische Methoden und künstliche Intelligenz ermöglichen eine präzise und frühzeitige Identifizierung potenzieller Schäden. Die Kombination aus kontinuierlichem Bauwerksmonitoring und effizienter Datenauswertung ist entscheidend für die langfristige Sicherheit und Langlebigkeit bestehender Brücken und trägt darüber hinaus zur Ressourcenschonung bei.
Bias Identification Approaches for Model Updating of Simulation-based Digital Twins of Bridges
(2024)
Simulation-based digital twins of bridges have the potential not only to serve as monitoring devices of the current state of the structure but also to generate new knowledge through physical predictions that allow for better-informed decisionmaking. For an accurate representation of the bridge, the underlying models must be tuned to reproduce the real system. Nevertheless, the necessary assumptions and simplifications in these models irremediably introduce discrepancies between measurements and model response. We will show that quantifying the extent of the uncertainties introduced through the models that lead to such discrepancies provides a better understanding of the real system, enhances the model updating process, and creates more robust and trustworthy digital twins. The inclusion of an explicit bias term will be applied to a representative demonstrator case based on the thermal response of the Nibelungenbrücke of Worms. The findings from this work are englobed in the initiative SPP 100+, whose main aim is the extension of the service life of structures, especially through the implementation of digital twins.
Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration
(2025)
A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. Nevertheless, Bayesian methods paired with inexact models usually produce predictions unable to represent the observed datapoints. Additionally, the quantified uncertainties of these overconfident models cannot be propagated to other Quantities of Interest (QoIs) reliably. A promising solution involves embedding a model inadequacy term in the inference parameters, allowing the quantified model form uncertainty to influence non-observed QoIs.
This paper introduces a more interpretable framework for embedding the model inadequacy compared to existing methods. To overcome the limitations of current approaches, we adapt the existing likelihood models to properly account for noise in the measurements and propose two new formulations designed to address their shortcomings.
Moreover, we evaluate the performance of this inadequacy-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction’s reliability. Finally, the proposed approach is applied to estimate the uncertainty
in the predicted heat flux from a transient thermal simulation using temperature bservations.