Jorg Unger

We present a new regularized inverse formulation, built upon the paradigm of force‐based Finite Element Model Updating (FEMU‐F), for identifying constitutive parameters from full‐field displacements and integrated external forces. In force‐based methods such as FEMU‐F and Equilibrium Gap Method (EGM), the minimized loss function is defined in terms of FE force residuals after imposing measured displacements as Dirichlet constraints. Such force‐based loss functions are known to be sensitive to noisy displacements. To overcome this limitation, we propose a new robust force‐based loss function that operates directly on elemental internal forces without assembling them into nodal values. The key innovation is the introduction of admissible elemental internal forces (AEIFs) as additional unknown variables into the proposed inverse formulation with inherent regularization effects. By definition, AEIFs satisfy static equilibrium with external forces, independent of kinematics and constitutive relations. The proposed loss function measures the discrepancy between AEIFs and the elemental internal forces computed from the FE model. The static equilibrium of AEIFs imposes purely force‐based admissibility conditions, which build a systematic reduction procedure delivering a compact, independent representation of the AEIFs space. This enables an analytical solution for unknown AEIFs when minimizing the proposed loss function, while the constitutive parameters are identified numerically. Numerical examples show that the regularized formulation reduces sensitivity to noisy full‐field displacements and inaccurate Dirichlet boundary conditions and mitigates FEMU‐F's bias toward local information near measured forces. Owing to its force‐only formulation, the method is simpler to implement than stress‐based approaches such as Constitutive Equation Gap (CEG). It identifies heterogeneous elasticity more accurately than FEMU‐F using a single measured integrated force. The method is also shown to remain robust in identifying parameters of a nonlinear, path‐dependent constitutive law with strain localization, highlighting its suitability for such challenging materials where measured full‐field displacements are directly imposed as Dirichlet constraints.

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. Bayesian methods provide a robust framework for quantifying and propagating the uncertainties that inevitably arise. Nevertheless, they produce predictions unable to represent the observed datapoints when paired with inexact models. 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. In this work, we revisit this embedded formulation and analyze how different likelihood constructions affect the inference of model form uncertainty, particularly under the presence of prescribed measurement noise and unavoidable model discrepancies. Two additional likelihood formulations, the global moment-matching and relative global moment-matching likelihoods, are introduced to explore alternative ways of representing the residual distribution. The behavior of these likelihoods is examined alongside existing formulations to show how different treatments of measurement noise and discrepancies shape the inferred parameter posteriors, and thereby affect the uncertainty ultimately propagated to the QoIs. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs for the posteriors obtained from different likelihood formulations, enabling a more comprehensive statistical analysis of the 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 observations.

Interlaboratory studies are essential for implementing standardized test methods for new innovative materials or technologies such as 3D concrete printing, certifying reference materials, and validating methods. They provide the basis for recommendations and design standards. Typically, the collected data are used only for one study and are published in paper form, without open access to the raw data files. However, preserving the collected data and analysis procedures in a shareable and reusable way leads to advantages for further usage. New data with the same structure can be added or the data can be analyzed by a new analysis procedure generating new knowledge. Additionally, resources are saved by avoiding the repetition of the same measurements. In line with the recent digitalization trend in material science and engineering, a database for the interlaboratory study on mechanical properties of 3D printed concrete conducted in RILEM Technical Committee 304-ADC is established. The challenges and experiences from defining the data structure, uploading the data, and using the database for evaluations are discussed. The openBIS software is used to create and fill the database, while an export method to an SQLite format is developed and discussed to generate a shareable and reusable database. With the exported database, querying the data is possible independently of a particular data management system. Additionally, examples of data processing with reusable workflows automating the analysis procedure are demonstrated.

. 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.