TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weise, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Bias Identification Approaches for Model Updating of Simulation-based Digital Twins of Bridges N2 - 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 decision-making. 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. T2 - EWSHM 2024 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Digital Twins KW - Model Bias KW - SPP100+ KW - Bridge Monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622522 DO - https://doi.org/10.58286/30524 SN - 2941-4989 IS - 12 SP - 1 EP - 10 PB - NDT.net GmbH & Co. KG CY - Mayen, Germany AN - OPUS4-62252 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - Uncertainty Quantification and Model Extension for Digital Twins of Bridges through Model Bias Identification N2 - The creation and use of Digital Twins of existing structures, such as bridges, implies precise digital replicas that accurately mirror their physical counterparts. Ensuring the trustworthiness of Digital Twins and facilitating informed decision-making necessitates a robust approach to Uncertainty Quantification (UQ). A suitable model-updating scheme is key in preserving the quality and robustness of simulation-based Digital Twins. Model bias, stemming from discrepancies between computational models and real-world systems, poses a significant challenge in achieving this goal. This study delves into the challenges posed by model bias within Bayesian updating of Digital Twins of bridges. Two alternative model bias identification methods —a modularized version of Kennedy and O’Hagan’s approach and another one based on Orthogonal Gaussian Processes — are evaluated in comparison with the classical Bayesian inference framework. A key innovation lies in the modification of the aforementioned approaches to incorporate additional information into the Digital Twin framework via the bias term. This enables the extension of the model non-intrusively, leveraging large pools of data inherent in Digital Twins. The study showcases the potential of this approach to correct predictions, quantify uncertainties, and enhance the system with previously untapped information. This underscores the importance of everaging available data within Digital Twins to identify deficiencies and guide potential future model improvements. T2 - ECCOMAS CONGRESS 2024 9th European Congress on Computational Methods in Applied Sciences and Engineering CY - Lisbon, Portugal DA - 03.06.2024 KW - Digital Twin KW - Simulation Models KW - Uncertainty Quantification KW - Model Bias PY - 2024 AN - OPUS4-61023 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, F. A1 - Unger, Jörg F. T1 - A Bayesian Framework for Simulation-based Digital Twins of Bridges N2 - Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their tructural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management. T2 - Eurostruct 2023 CY - Vienna, Austria DA - 25.09.2023 KW - Digital Twins KW - Bayesian Inference KW - Bridge Monitoring KW - Uncertainty Quantification PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-586803 UR - https://eurostruct.org/eurostruct-2023/ DO - https://doi.org/10.1002/cepa.2177 SN - 2509-7075 VL - 6 IS - 5 SP - 734 EP - 740 PB - Ernst & Sohn CY - Berlin AN - OPUS4-58680 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Munsch, Sarah Mandy A1 - Telong, Melissa A1 - Unger, Jörg F. A1 - Andrés Arcones, Daniel A1 - Pirskawetz, Stephan T1 - 63. DAfStb-Forschungskolloquium in der BAM - Themenblock 4: Digitalisierung im Bauwesen N2 - Die Digitalisierung hat sich in vielen Bereichen des Bauwesens durchgesetzt. So sind Planung und Entwurf selbst kleinerer Bauvorhaben heute vollständig digitalisiert. Auch das Monitoring von Bestandsbauwerken ist ohne digitale Datenerfassung, -verarbeitung und -speicherung nicht denkbar. Trotzdem sind Fragen hinsichtlich der strukturierten Speicherung und künftigen Nutzung von Daten noch offen. Einige Aspekte der Digitalisierung wurden im Rahmen des 63. DAfStb-Forschungskolloquiums (Tagungsband: DOI 10.26272/opus4-61338) in Vorträgen und Veröffentlichungen aufgegriffen und werden im Folgenden zusammengefasst. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Digitalisierung KW - Infrastruktur KW - Structural Health Monitoring KW - Digitaler Zwilling PY - 2025 SN - 0005-9846 VL - 75 IS - 4 SP - 136 EP - 138 PB - concrete content UG CY - Schermbeck AN - OPUS4-63070 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - Modell- und Parameterunsicherheiten am Beispiel eines Digitalen Brückenzwillings N2 - Digitale Zwillinge bieten wertvolle Einblicke in das Verhalten von Bauwerken und ermöglichen eine fundierte Entscheidungsfindung. Durch den Einsatz von Simulationen, die auf physikalischen Gesetzen beruhen, ist es möglich, Vorhersagen über die Struktur auf der Grundlage zukünftiger oder hypothetischer Situationen zu treffen. Die Verwendung solcher Simulationen impliziert jedoch eine Reihe von Annahmen und Vereinfachungen, die unvermeidbare Fehler in die Vorhersagen einbringen. Die Quantifizierung dieser Unsicherheiten ist der Schlüssel für den Einsatz zuverlässiger digitaler Zwillinge von Brücken. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Unsicherheiten Quantifizierung KW - Brückenüberwachung KW - Digitaler Zwilling PY - 2024 AN - OPUS4-61568 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - Evaluation of Model Bias Identification Approaches Based on Bayesian Inference and Applications to Digital Twins N2 - In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat- FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed. T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantificationin Computational Sciences and Engineering CY - Athens, Greece DA - 12.06.2023 KW - Digital Twin KW - Simulation Models KW - Uncertainty Quantification KW - Model Bias PY - 2023 UR - https://2023.uncecomp.org/ AN - OPUS4-58226 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - Incorporating model form uncertainty in digital twins for reliable parameter updating and quantitites of interest analysis N2 - With the rapid adoption of Digital Twins in recent years, simulation models designed to replicate real-world physical systems have become increasingly common. To achieve accurate representations, it is typically necessary to update model parameters based on observations collected from sensors or measurements of the physical asset. However, no model can fully capture the infinitely complex nature of reality. As a result, quantifying the uncertainty in model predictions is essential for reliable decision-making. Bayesian updating frameworks provide an appealing approach for parameter calibration, inherently accounting for such uncertainties. One often-overlooked source of error is model form uncertainty. This type of uncertainty arises from the fundamental discrepancies between the model and reality, stemming from the assumptions and simplifications made during model construction. Ignoring model form uncertainty can lead to overly confident predictions that fail to accurately reflect sensor observations. To address this, we propose an embedded model form uncertainty framework that attributes the model variability to a stochastic extension of the model's latent parameters. This approach enables the quantification of uncertainties that can be represented by a variation in the model parameters. Of particular interest are scenarios involving noisy observations or additional discrepancies that cannot be directly integrated into the model. By incorporating uncertainty through the parameters, this method not only quantifies uncertainty in predictions but also propagates model form uncertainty to other Quantities of Interest (QoI) that rely on the same model or its parameters. Consequently, QoI computations yield more reliable values, accounting for the potential uncertainties introduced by imperfect models during parameter updating. Moreover, this approach facilitates a more comprehensive statistical analysis of QoI distributions, offering deeper insights into the model's reliability and highlighting areas for potential improvement. By incorporating model form uncertainty, decision-makers can achieve a more robust and nuanced understanding of system behavior and prediction quality. T2 - 95th Annual Meeting oft eh International Association of Applied Mathematics and Mechanics (GAMM) CY - Poznan, Poland DA - 07.04.2024 KW - Unsicherheiten Quantifizierung KW - Brückenüberwachung KW - Digitaler Zwilling PY - 2025 AN - OPUS4-62972 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Becks, H. A1 - Lippold, L. A1 - Winkler, P. A1 - Moeller, M. A1 - Rohrer, M. A1 - Leusmann, T. A1 - Anton, D. A1 - Sprenger, B. A1 - Kähler, P. A1 - Rudenko, I. A1 - Andrés Arcones, Daniel A1 - Koutsourelakis, P. A1 - Unger, Jörg F. A1 - Weiser, M. A1 - Petryna, Y. A1 - Schnellenbach-Held, M. A1 - Lowke, D. A1 - Hessels, H. A1 - Lenzen, A. A1 - Zabel, V. A1 - Könke, C. A1 - Claßen, M. A1 - Hegger, J. T1 - Neuartige Konzepte für die Zustandsüberwachung und -analyse von Brückenbauwerken – Einblicke in das Forschungsvorhaben SPP100+ N2 - 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. KW - Bauwerkserhaltung KW - Brückenbau KW - Monitoring KW - Lebensdauer PY - 2024 DO - https://doi.org/10.37544/0005-6650-2024-10-63 SN - 0005-6650 VL - 99 IS - 10 SP - 327 EP - 338 PB - VDI Verlag-eLibrary - technisches Wissen für Ingenieur*innen CY - Düssledorf AN - OPUS4-61563 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P.-S. A1 - Unger, Jörg F. T1 - Model bias identification for Bayesian calibration of stochastic digital twins of bridges N2 - Simulation-based digital twins must provide accurate, robust, and reliable digital representations of their physical counterparts. Therefore, quantifying the uncertainty in their predictions plays a key role in making better-informed decisions that impact the actual system. The update of the simulation model based on data must then be carefully implemented. When applied to complex structures such as bridges, discrepancies between the computational model and the real system appear as model bias, which hinders the trustworthiness of the digital twin and increases its uncertainty. Classical Bayesian updating approaches aimed at inferring the model parameters often fail to compensate for such model bias, leading to overconfident and unreliable predictions. In this paper, two alternative model bias identification approaches are evaluated in the context of their applicability to digital twins of bridges. A modularized version of Kennedy and O'Hagan's approach and another one based on Orthogonal Gaussian Processes are compared with the classical Bayesian inference framework in a set of representative benchmarks. Additionally, two novel extensions are proposed for these models: the inclusion of noise-aware kernels and the introduction of additional variables not present in the computational model through the bias term. The integration of these approaches into the digital twin corrects the predictions, quantifies their uncertainty, estimates noise from unknown physical sources of error, and provides further insight into the system by including additional pre-existing information without modifying the computational model. KW - Gaussian process KW - KOH KW - Bayesian updating KW - Digital twins KW - Uncertainty quantification PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615519 DO - https://doi.org/10.1002/asmb.2897 SN - 1526-4025 N1 - This work was supported by “C07 - Data driven model adaptation for identifying stochastic digital twins of bridges” from the Priority Program (SPP) 2388/1 “Hundred plus” of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) - Project number 501811638. VL - 417 IS - 3 SP - 1 EP - 26 PB - Wiley CY - Chichester AN - OPUS4-61551 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Andrés Arcones, Daniel A1 - Diercks, Philipp A1 - Robens-Radermacher, Annika A1 - Rosenbusch, Sjard Mathis A1 - Tamsen, Erik A1 - Tyagi, Divyansh A1 - Unger, Jörg F. T1 - FenicsXConcrete N2 - FenicsXConcrete is a Python package for the simulation of mechanical problems. The general PDE solving software FEniCSx is extended with classes describing experimental setups, mechanical problems, thermo-mechanical problems, additive manufacturing and sensors. KW - FEM KW - Fenics KW - Concrete modelling PY - 2023 UR - https://github.com/BAMresearch/FenicsXConcrete DO - https://doi.org/10.5281/zenodo.7780757 PB - Zenodo CY - Geneva AN - OPUS4-59121 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - A Bayesian Framework for Simulation-based Digital Twins of Bridges N2 - Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their structural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management. T2 - EUROSTRUCT 2023 CY - Vienna, Austria DA - 25.09.2023 KW - Digital Twin KW - Simulation Models KW - Uncertainty Quantification KW - Model Bias PY - 2023 UR - https://eurostruct.org/eurostruct-2023/ AN - OPUS4-58663 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - Bias Identification Approaches for Model Updating of Simulation-based Digital Twins of Bridges N2 - 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 decision-making. For an accurate representation of the bridge, the underlying models must be tuned to reproduce the real system. The updated model can be eventually used for the extension of the service life of the bridge based on an accurate description of the structure. Nevertheless, the necessary assumptions and simplifications in these models irremediably introduce discrepancies between measurements and model response. We will prove that quantifying the extent of the uncertainties generated by said discrepancies provides a better understanding of the real system, enhances the model updating process, and creates more robust and trustworthy digital twins. Among others, we identify that the inclusion of the explicit bias term through a Bayesian inference framework corrects the tuned parameters, allows the identification of non-prescribed noise sources and enables the introduction of additional information in the system without modifying the simulation model. The performance of selected model bias identification approaches will be compared in the context of digital twins of bridges. The different methods will be applied to a representative demonstrator case based on 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 through monitorization and digitalization, especially through the implementation of digital twins. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Digital Twin KW - Simulation Models KW - Uncertainty Quantification KW - Model Bias PY - 2024 AN - OPUS4-61024 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, F.-S. A1 - Unger, Jörg F. T1 - Evaluation of Model Bias Identification Approaches Based on Bayesian Inference and Applications to Digital Twins N2 - In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model. This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed. T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantificationin Computational Sciences and Engineering CY - Athen, Greece DA - 12.06.2023 KW - Model bias KW - Bayesian Uncertainty Quantification KW - Digital Twins KW - Gaussian Processes KW - Statistical Finite Element Method PY - 2023 UR - https://2023.uncecomp.org/ SP - 1 EP - 15 AN - OPUS4-58227 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Quantifying the uncertainty of predictive simulations in digital twins through the identification of model bias N2 - This work presents a novel approach to quantifying uncertainty in digital twin simulations by addressing model bias through embedded parameter distributions. Traditional Bayesian methods often underestimate uncertainty due to assumptions of model correctness. We propose a hierarchical Bayesian framework combined with Polynomial Chaos Expansion to better capture and propagate uncertainty. The methodology is validated on an analytical example and a real-world case involving thermal deformation predictions of the Nibelungen Bridge, demonstrating improved predictive accuracy and reliability. T2 - fib Symposium 2025 CY - Antibes, France DA - 16.06.2025 KW - Digital Twins KW - Model Bias KW - Predictive simulations KW - Quantifying the uncertainty PY - 2025 SP - 2867 EP - 2873 PB - The fib, Fédération international du béton CY - Antibes, France AN - OPUS4-63629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - Embedding model form uncertainties for Bayesian inference of discrepant simulations N2 - Simulation models are widely used to generate valuable insights into complex physical systems. To accurately reflect system behavior, these models require updates to their governing parameters based on system measurements. Bayesian inference methodologies are particularly attractive for this purpose, as they quantify parameter uncertainty. However, simulation models inherently exhibit discrepancies with observed measurements, as they cannot perfectly replicate the infinitely complex reality. Ignoring these discrepancies leads to overconfident estimations of the inferred posterior distributions, potentially centering around incorrect parameters. This issue affects the calculation of predictions and Quantities of Interest (QoIs), resulting in overly concentrated posterior distributions. The most common framework for incorporating model form uncertainty was developed by Kennedy and O’Hagan, which introduces a flexible discrepancy term that is inferred alongside model parameters. However, this approach does not preserve the physicality of the predictions nor facilitate the propagation of model form uncertainty to other QoIs. To address these limitations, Sargsyan proposed embedding the discrepancy term in the parameter formulation as a stochastic extension. This work demonstrates our own explainable framework for embedding model form uncertainties in the Bayesian system of complex engineering systems. Our framework emphasizes the interpretability of discrepancy terms, quantifies uncertainties for models with significant discrepancies relative to measurements and high noise levels, and fully propagates these uncertainties to QoIs for reliable statistical analysis. We apply this framework to a thermal compensation model for the structural health monitoring system of a bridge, illustrating its potential for enhancing decision-making in engineering applications. T2 - UNCECOMP 2025 CY - Rhodes, Greece DA - 15.06.2025 KW - Unsicherheiten Quantifizierung KW - Brückenüberwachung KW - Digitaler Zwilling PY - 2025 AN - OPUS4-64353 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, P-S. A1 - Unger, Jörg F. T1 - Embedded Model Bias Quantification with Measurement Noise for Bayesian Model Calibration N2 - 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 observations. KW - Model bias KW - Bayesian inference KW - Noise KW - Model updating KW - Quantity of Interest PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-652720 DO - https://doi.org/10.48550/arXiv.2410.12037 SP - 1 EP - 37 PB - arXiv.org AN - OPUS4-65272 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kang, Chongjie A1 - Arcones, Daniel Andrés A1 - Becks, Henrik A1 - Beetz, Jakob A1 - Blankenbach, Jörg A1 - Claßen, Martin A1 - Degener, Sebastian A1 - Eisermann, Cedric A1 - Göbels, Anne A1 - Hegger, Josef A1 - Hermann, Ralf A1 - Kähler, Philipp A1 - Peralta, Patricia A1 - Petryna, Yuri A1 - Schnellenbach‐Held, Martina A1 - Schulz, Oliver A1 - Smarsly, Kay A1 - Fatih Sönmez, Mehmet A1 - Sprenger, Bjarne A1 - Unger, Jörg F. A1 - Vassilev, Hristo A1 - Weiser, Martin A1 - Marx, Steffen T1 - Intelligente digitale Methoden zur Verlängerung der Nutzungsdauer der Nibelungenbrücke N2 - Um die Lebensdauer von Bauwerken unter Wahrung derer Standsicherheit und Funktionsfähigkeit zu verlängern, bedarf es effektiver Monitorings‐ sowie Instandhaltungskonzepte. Im Rahmen des von der Deutschen Forschungsgemeinschaft (DFG) geförderten Schwerpunktprogramms 2388 „Hundert plus – Verlängerung der Lebensdauer komplexer Baustrukturen durch intelligente Digitalisierung“ (kurz: SPP 100+) werden hierfür innovative, interdisziplinäre Methoden entwickelt und an der Nibelungenbrücke in Worms (NBW) validiert. Der vorliegende Beitrag stellt einige dieser neuentwickelten digitalen Methoden vor. Unter anderem umfasst dies zwei Systeme des Structural Health Monitoring (SHM) und deren zielorientierte Verknüpfung von mehreren Beschleunigungsmessdaten zur umfassenden Zustandsbewertung. Ergänzend werden innovative datenbasierte Simulationsmethoden zur Bestimmung des Temperaturfelds des Brückenüberbaus vorgestellt sowie mehrere Finite‐Elemente‐Modelle unterschiedlicher Detailtiefe präsentiert und miteinander verglichen. Abschließend werden innovative Methoden zum Verwalten des Bestandswissens von Brückenbauwerken diskutiert. Die Methoden wurden überwiegend unabhängig voneinander entwickelt und an der NBW validiert. Im nächsten Schritt werden die Methoden integriert, um die Instandhaltung der NBW zu unterstützen. KW - Nibelungenbrücke Worms KW - Digitaler Zwilling KW - Prädiktive Instandhaltung KW - Bauwerksmonitoring KW - Nachrechnung KW - Verkehrsinfrastruktur KW - FE-Modell PY - 2025 DO - https://doi.org/10.1002/best.70070 SN - 0005-9900 VL - 121. Jahrgang 2026 SP - 1 EP - 18 PB - Ernst & Sohn a Wiley Brand CY - Wien, Austria AN - OPUS4-65288 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel T1 - Quantifying Model Form Uncertainty, Influence, and Separability in Data-Driven Calibration of Tendon Break Models N2 - Fiber optic sensors (FOS) are increasingly used for structural health monitoring of bridges, providing dense and distributed strain measurements. An especially relevant application is the detection and localization of tendon breaks, where not only the occurrence but also the depth of the damaged tendon must be identified. To explore this possibility, an experimental setup was established to reproduce the boundary and loading conditions of a bridge tendon, and FOS strain observations were collected during controlled failure events. A finite element (FE) model of this experiment was developed and is used here as a gray-box simulator. Its parameters are calibrated against the FOS data, and the resulting probabilistic characterization is transferred to more realistic models in which different tendon depths are simulated and compared to observed deformation fields. Because of the complexity of the tendon break phenomenon, model form uncertainty (MFU) plays a decisive role. Bayesian calibration is performed while explicitly accounting for MFU through an embedded representation in which a prescribed set of parameters captures the model–reality discrepancy. This formulation enables the quantified MFU to be consistently propagated to other models and quantities of interest. Due to the computational cost of FE simulations, Gaussian Process surrogates are employed, and their predictive uncertainty is incorporated into the calibration to ensure coherent uncertainty propagation. The calibration reveals that some observations cannot be represented by any admissible parameter values, indicating structural model inadequacies. To identify and characterize these, an influence analysis of the observations on the posterior distribution is carried out. The φ-divergence between posterior distributions with and without individual observations is evaluated to quantify their impact on the calibrated posterior. Furthermore, by examining the influence on the marginal posterior components within the embedded MFU formulation, it becomes possible to determine which observations drive the parameters associated with model discrepancy and which parameters are most affected, guiding model refinement and experimental design. Finally, when the calibrated parameters are transferred to alternative models, the separability of predictions for different tendon depths is analyzed. The overlap between predictive distributions is quantified to determine the minimum resolvable depth difference and regions of non-separability arising from MFU. This analysis defines the achievable resolution for tendon break localization and informs optimal sensor placement strategies under uncertainty. T2 - 96. Jahrestagung der GAMM CY - Stuttgart, Germany DA - 16.03.2026 KW - Unsicherheiten Quantifizierung KW - Brückenüberwachung KW - Digitaler Zwilling PY - 2026 AN - OPUS4-65789 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -