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 - 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 - 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 -