Open Access

Simulation-based digital twins are emerging as key enablers for extending the service life of bridges through data-driven assessment, performance prediction, and proactive maintenance planning. They integrate a numerical simulation model with sensor observations to estimate condition indicators and predict future performance. Reliable prognostics, however, require that model parameters are continuously updated to reflect the evolving state of the structure. Despite this, assumptions made during model formulation introduce model form uncertainty that leads to systematic discrepancies between predictions and measurements. This contribution presents an embedded approach for quantifying and propagating model form uncertainty in bridge digital twins. The method attaches the uncertainty to the updated parameters during the model updating process, ensuring its consistent propagation to all relevant quantities of interest, including condition indicators and prognostic outcomes. This approach allows predictions to be expressed as probabilistic distributions rather than deterministic scalar values, providing a richer characterization of prediction reliability and improved decision support. By explicitly representing model form uncertainty, the framework enhances both the interpretability and trustworthiness of simulation-based assessments. The methodology is applied to the Nibelungenbrücke in Worms, Germany. Results demonstrate that accounting for model form uncertainty improves agreement between simulated and observed responses and provides more credible prognostics for long-term performance management. The proposed framework contributes to developing reliable and trustworthy digital twins as intelligent tools for extending the operational life of critical infrastructure.

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.

The geometric morphology of deposited filaments can significantly influence the structural performance and stability of 3D concrete-printed (3DCP) structures. However, most finite element (FEM)-based approaches for buildability assessment represent printed layers as simplified rectangles, potentially limiting predictive accuracy. This study proposes a geometry-informed modelling framework that integrates the deep-learning-based filament shape prediction tool ShapeGen3DCP with a layer-activation FEM approach to investigate the effect of realistic filament geometries on buildability. The framework generates geometry-aware numerical models directly from material and process parameters, eliminating the need for experimental filament characterization or computationally intensive fluid-flow simulations. Validation against experimental data and a parametric study of rectilinear walls demonstrate that extrusion parameters and the resulting filament geometry can significantly influence buildability predictions. Realistic filament representations are particularly important for free-flow deposition, whereas layer-pressing strategies are less sensitive to geometric simplifications. Among the investigated representations, an elliptical approximation provides an effective balance between geometric fidelity and modelling simplicity. When rectangular representations are preferred to enable regular computational meshes for faster simulations, defining their dimensions based on volume conservation improves prediction reliability compared with calibrating them using either the maximum filament width or the interlayer contact width. Overall, the proposed methodology demonstrates the importance of incorporating filament geometry into 3DCP simulations and provides practical guidance for selecting efficient and accurate geometric representations for buildability assessment.

3D concrete printing (3DCP) aims to revolutionize construction by increasing automation, reducing material usage, and enabling customized designs. However, the absence of standardized regulations and the common reliance on trial-and-error approaches lead to significant waste and inefficiencies. Reliable predictive models are therefore required to better understand and control the complex printing process, which is influenced by material properties, process parameters, and environmental conditions. This study aims to develop a structural model that predicts print stability to prevent buckling and material failure in extrusion-based 3D concrete printing (3DCP), with a particular focus on environmental influences, especially temperature. Temperature affects the evolution of material stiffness and strength, and thus directly impacts buildability in the fresh state. To capture these effects, a time- and temperature-dependent model for the development of material parameters is integrated into a structural simulation of the printing process. The model employs an elastoplastic material law with nonlinear hardening, and layers are activated sequentially using a pseudo-density approach. The methodology is demonstrated using an example of a single-layer-width printed cylinder subjected to varying ambient temperatures. The influence of temperature on buckling behavior and material failure during printing is analyzed and quantified through a numerical sensitivity study. The results show the strong influence of ambient conditions on buildability and provide a robust basis for future studies and process optimization in 3D concrete printing.

3D concrete printing in powder-bed enables realization of complex geometries even with overhangs or undercuts. The extrusion-based 3D print technology gains positive acceptance, but safety needed. In-situ testing for ensuring the quality of the process Optimization end development of 3D print technology.