Embedding model uncertainty improves bridge digital twin predictions
Embedding Model-form Uncertainty in Probabilistic Calibration of Digital Twins for Bridges
Computational Engineering, Finance, and Science
Summary
Bridges are monitored using digital twins, which use computer models to predict how the structure behaves based on limited sensor data. These models often simplify real-world behavior, leading to uncertainty in predictions called model-form uncertainty. The authors developed a method to include this uncertainty explicitly during the model calibration process, making the predictions more reliable. They tested their method on a bridge's temperature model and showed it produces better and clearer uncertainty estimates, improving trust in digital twin predictions.
What this means in practice
- •For bridge maintenance teams: Use the method to improve digital twin predictions of bridge conditions from sparse sensor data by explicitly handling model uncertainties.
- •For infrastructure monitoring engineers: Calibrate physics-based structural models more reliably by embedding model uncertainties during data-driven updates, enhancing monitoring accuracy.
Authors
Daniel Andrés Arcones, Martin Weiser, Phaedon-Stelios Koutsourelakis, Jörg F. Unger
Abstract
Digital twins of bridges rely on physics-based models to infer full-field structural responses from sparse monitoring data. The reliability of these predictions depends on the calibration of model parameters while accounting for discrepancies between the model and the physical system. Such discrepancies, commonly referred to as model-form uncertainty (MFU), arise from simplifying assumptions and incomplete representations of physical processes, and can substantially affect predictive reliability. Explicitly quantifying MFU during calibration is therefore essential for trustworthy digital-twin predictions. This work presents a calibration framework that explicitly represents MFU through stochastic parameter embedding. The framework is demonstrated using a simplified two-dimensional cross-sectional thermal model of the Nibelungenbrücke calibrated against temperature measurements from its monitoring system. By explicitly accounting for MFU, the proposed methodology enables computationally efficient models to be reliably employed within digital-twin environments while maintaining predictive credibility. The framework introduces a three-step calibration strategy that progressively addresses sources of discrepancy while maintaining a clear distinction between identified uncertainties. Variance decomposition is used to characterize the structure of predictive uncertainty and its propagation to quantities of interest. Remaining discrepancies between predictive distributions and observations are quantified using Kolmogorov-Smirnov metrics to assess predictive consistency across seasonal conditions. The results demonstrate that explicitly accounting for MFU improves the interpretability and reliability of predictive uncertainties and validate the methodology for calibration of physics-based models supporting digital twins in structural health monitoring.