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 - JOUR A1 - Bekemeier, Simon A1 - Caldeira Rêgo, C. R. A1 - Mai, H. L. A1 - Sikia, Ujjal A1 - Waseda, O. A1 - Apel, M. A1 - Arendt, F. A1 - Aschemann, A. A1 - Bayerlein, Bernd A1 - Courant, R. A1 - Dziwis, G. A1 - Fuchs, F. A1 - Giese, U. A1 - Junghanns, K. A1 - Kamal, M. A1 - Koschmieder, L. A1 - Leineweber, S. A1 - Luger, M. A1 - Lukas, M. A1 - Maas, J. A1 - Mertens, J. A1 - Mieller, Björn A1 - Overmeyer, L. A1 - Pirch, N. A1 - Reimann, J. A1 - Schröck, S. A1 - Schulze, P. A1 - Schuster, J. A1 - Seidel, A. A1 - Shchyglo, O. A1 - Sierka, M. A1 - Silze, F. A1 - Stier, S. A1 - Tegeler, M. A1 - Unger, Jörg F. A1 - Weber, M. A1 - Hickel, Tilmann A1 - Schaarschmidt, J. T1 - Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative N2 - The MaterialDigital initiative represents a major driver toward the digitalization of material science. Next to providing a prototypical infrastructure required for building a shared data space and working on semantic interoperability of data, a core focus area of the Platform MaterialDigital (PMD) is the utilization of workflows to encapsulate data processing and simulation steps in accordance with findable, accessible, interoperable, and reusable principles. In collaboration with the funded projects of the initiative, the workflow working group strives to establish shared standards, enhancing the interoperability and reusability of scientific data processing steps. Central to this effort is the Workflow Store, a pivotal tool for disseminating workflows with the community, facilitating the exchange and replication of scientific methodologies. This article discusses the inherent challenges of adapting workflow concepts, providing the perspective on developing and using workflows in the respective domain of the various funded projects. Additionally, it introduces the Workflow Store’s role within the initiative and outlines a future roadmap for the PMD workflow group, aiming to further refine and expand the role of scientific workflows as a means to advance digital transformation and foster collaborative research within material science. KW - Digitalisation KW - FAIR principles KW - MaterialDigital KW - Scientific workflows KW - Semantic interoperability PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624088 DO - https://doi.org/10.1002/adem.202402149 SN - 1527-2648 IS - 2402149 SP - 1 EP - 25 PB - Wiley-VCH GmbH AN - OPUS4-62408 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 - CONF A1 - Meng, Birgit A1 - Dehn, F. A1 - Unger, Jörg F. A1 - Shepherd, D. A. A1 - Tamsen, Erik A1 - Pirskawetz, Stephan T1 - Wissensbasierte Digitalisierung von betontechnologischen Materialdaten N2 - Die sprunghaft zunehmende Wichtigkeit von FAIR- und Open-Data für die Qualitätssicherung, aber auch für die Nachnutzbarkeit von Daten und den Erkenntnisfortschritt führt zu enormem Flandlungsbedarf in Forschung und Entwicklung. Damit verbunden laufen derzeit vielfältige, ambitionierte Aktionen, z. B. bezüglich der Erstellung von Ontologien und Wissensgraphen. Das Knowhow entwickelt sich rasant, die Ansätze zur Implementation entstehen in verschiedenen Fachwelten bzw. mit unterschiedlichen Zielsetzungen parallel, so dass recht heterogene Herangehensweisen resultieren. Diese Veröffentlichung fokussiert auf Arbeiten, die derzeit als möglichst ganzheitlicher Ansatz für Materialdaten im Rahmen der Digitalisierungsinitiative „Plattform MaterialDigital" vorangetrieben werden. Die Autoren bearbeiten baustoffbezogene Aspekte im Verbundprojekt „LeBeDigital - Lebenszyklus von Beton". Zielsetzung ist die digitale Beschreibung des Materialverhaltens von Beton über den kompletten Herstellungsprozess eines Fertigteils mit einer Integration von Daten und Modellen innerhalb eines Workflows zur probabilistischen Material- und Prozessoptimierung. Es wird über die Vorgehensweise und die dabei gewonnenen Erfahrungen berichtet, nicht ohne den Blick auf die oft unterschätzte Komplexität der Thematik zu lenken. T2 - Internationale Baustofftagung (ibausil) CY - Weimar, Germany DA - 13.09.2023 KW - Beton KW - Digitalisierung KW - Materialdaten KW - Datenmanagement KW - FAIR-Prinzip KW - Metadaten KW - Ontologie KW - Wissengraph PY - 2023 UR - https://www.uni-weimar.de/de/bauingenieurwesen/institute/fib/ibausil/ VL - 2023 SP - 1 EP - 11 PB - Sonderausgabe des Online-Journals ce/papers von Wiley AN - OPUS4-58315 LA - deu 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 - JOUR A1 - Kindrachuk, Vitaliy A1 - Titscher, Thomas A1 - Unger, Jörg F. T1 - A Fourier transformation-based method for gradient-enhanced modeling of fatigue N2 - A key limitation of the most constitutive models that reproduce a Degradation of quasi-brittle materials is that they generally do not address issues related to fatigue. One reason is the huge computational costs to resolve each load cycle on the structural level. The goal of this paper is the development of a temporal Integration scheme, which significantly increases the computational efficiency of the finite element method in comparison to conventional temporal integrations. The essential constituent of the fatigue model is an implicit gradient-enhanced formulation of the damage rate. The evolution of the field variables is computed as amultiscale Fourier series in time.On a microchronological scale attributed to single cycles, the initial boundary value problem is approximated by linear BVPs with respect to the Fourier coefficients. Using the adaptive cycle jump concept, the obtained damage rates are transferred to a coarsermacrochronological scale associated with the duration of material deterioration. The performance of the developedmethod is hence improved due to an efficient numerical treatment of the microchronological problem in combination with the cycle jump technique on the macrochronological scale. Validation examples demonstrate the convergence of the obtained solutions to the reference simulations while significantly reducing the computational costs. KW - Accelerated temporal integration KW - Fourier series KW - Gradient-enhanced fatigue model PY - 2018 DO - https://doi.org/10.1002/nme.5740 SN - 1097-0207 SN - 0029-5981 VL - 114 IS - 2 SP - 196 EP - 214 PB - Wiley AN - OPUS4-44008 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Otto, Peter A1 - De Lorenzis, L. A1 - Unger, Jörg F. T1 - Coupling a NURBS contact interface with a higher order finite element discretization for contact problems using the mortar method N2 - In this paper, a contact problem between two bodies, discretized by finite elements, is solved by adding an auxiliary NURBS layer between the bodies. The advantages of a smooth contact formulation in a NURBS approach are combined with simple mesh generation procedures for the bodies discretized with finite elements. Mesh tying conditions are used to couple the NURBS layer with the finite element discretization. The NURBS layer is the master side for contact and mesh tying. Mesh tying is enforced either using pointwise or mortar type approaches. Frictionless 2D and 3D contact problems are considered using small deformations. The contact problem is discretized with the mortar method and a penalty approach is used to enforce the contact constraints. A robust element-based quadrature is applied for mortar tying and contact discretizations, thus avoiding computationally expensive Segmentation. KW - Contact mechanics KW - Isogeometric analysis KW - Mortar methods KW - Spectral element method PY - 2018 DO - https://doi.org/10.1007/s00466-018-1645-y SN - 0178-7675 SN - 1432-0924 VL - 63 IS - 6 SP - 1203 EP - 1222 PB - Springer AN - OPUS4-46800 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - He, Yuxiang A1 - Robens-Radermacher, Annika A1 - Noda, S. A1 - Wolf, Christoph A1 - Unger, Jörg F. A1 - Mai, I. ED - Ferrara, L. ED - Muciaccia, G. ED - di Summa, D. T1 - Automated Workflows for Concrete Additive Manufacturing for Design, Optimization, and Fabrication of Parametrized Elements N2 - 3D concrete printing is an innovative new construction technology offering the potential to enable the efficient production of individual structures with less consumption of resources. The technology will mainly shape the future construction philosophy. From the design of a structure to the printed component, many individual steps based on different software are required, which must be repeated for each new or even slightly changed design. The geometry of the structure is created in a CAD program. The print path is defined in slicer software leading to the machine code for the printer to print the structure. A numerical model of the printed structure makes optimization in design and fabrication possible, by predicting the behaviour of the structure and reducing the number of test prints and costs. For that, additional steps like meshing the design and running a simulation are required. In order to work efficiently, an automated workflow is necessary, which runs all of the individual steps without interacting with each software program. Furthermore, changes in parameters or the exchange of parts (different designs or printers) must be simple. One way to develop such an automated workflow is presented within this paper. The interfaces are defined in a way that allows running the full chain of tools as well as individual steps. The workflow is demonstrated based on the example of a parametrized wall element for extrusion-based concrete. Furthermore, a test series of cubes is printed, and the influence of different infill structures is numerically and experimentally compared. T2 - RSCC 2024 - RILEM Spring Convention and Conference 2024 CY - Milano, Italy DA - 07.04.2024 KW - 3D concrete printing KW - Automation KW - Workflow KW - Innovative technologies PY - 2024 SN - 978-3-031-70280-8 SN - 978-3-03170281-5 SN - 978-3-031-70283-9 DO - https://doi.org/10.1007/978-3-031-70281-5_28 SN - 2211-0844 SN - 2211-0852 N1 - Serientitel: RILEM Bookseries – Series title: RILEM Bookseries VL - 56 IS - 2 SP - 247 EP - 254 PB - Springer CY - Cham AN - OPUS4-61608 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Titscher, Thomas A1 - van Dijk, T. A1 - Kadoke, Daniel A1 - Robens-Radermacher, Annika A1 - Herrmann, Ralf A1 - Unger, Jörg F. T1 - Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator N2 - Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies. KW - Damage detection KW - Finite element analysis KW - Load identification KW - Model updating KW - Estimation KW - System identification KW - Variational Bayesian statistics PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-574496 DO - https://doi.org/10.1002/eng2.12669 SN - 2577-8196 SP - 1 EP - 27 PB - Wiley CY - Hoboken, NJ AN - OPUS4-57449 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Diercks, Philipp A1 - Veroy, K. A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction N2 - In this paper, a methodology for fine scale modeling of large scale linear elastic structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modelled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Local reduced spaces are constructed by solving an oversampling problem with random boundary conditions. Herein, we inform the boundary conditions by a global reduced problem and compare our approach using physically meaningful correlated samples with existing approaches using uncorrelated samples. The local spaces are designed such that the local contribution of each subdomain can be coupled in a conforming way, which also preserves the sparsity pattern of standard finite element assembly procedures. Several numerical experiments show the accuracy and efficiency of the method, as well as its potential to reduce the size of the local spaces and the number of training samples compared to the uncorrelated sampling. KW - Multiscale methods KW - Variational multiscale method KW - Localized model order reduction KW - Domain decomposition methods PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580671 DO - https://doi.org/10.1002/nme.7326 SN - 0029-5981 SP - 1 EP - 23 PB - Wiley online library AN - OPUS4-58067 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Robens-Radermacher, Annika A1 - Held, Felix A1 - Coelho Lima, Isabela A1 - Titscher, Thomas A1 - Unger, Jörg F. T1 - Efficient identification of random fields coupling Bayesian inference and PGD reduced order model for damage localization N2 - One of the main challenges regarding our civil infrastructure is the efficient operation over their complete design lifetime while complying with standards and safety regulations. Thus, costs for maintenance or replacements must be optimized while still ensuring specified safety levels. This requires an accurate estimate of the current state as well as a prognosis for the remaining useful life. Currently, this is often done by regular manual or visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. Model‐based approaches can be used where a digital twin of the structure is set up. For these approaches, a key challenge is the calibration and validation of the numerical model based on uncertain measurement data. The aim of this contribution is to increase the efficiency of model updating by using the advantage of model reduction (Proper Generalized Decomposition, PGD) and applying the derived method for efficient model identification of a random stiffness field of a real bridge.” KW - Model reduction KW - Model updating KW - Proper generalized decomposition KW - Random field KW - Variational Bayesian Inference PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-521275 DO - https://doi.org/10.1002/pamm.202000063 VL - 20 IS - 1 SP - e202000063 PB - Wiley Online Libary AN - OPUS4-52127 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Strobl, Dominic A1 - Unger, Jörg F. A1 - Ghnatios, C. A1 - Robens-Radermacher, Annika T1 - PGD in thermal transient problems with a moving heat source: A sensitivity study on factors affecting accuracy and efficiency N2 - Thermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model’s performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non-separation yields the best approximation to the full order model. KW - Additive manufacturing KW - Mapping for unseparable load KW - Model order reduction (MOR) KW - Thermal transient problem KW - Sensitivity analysis KW - Proper generalized decomposition (PGD) PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-598001 DO - https://doi.org/10.1002/eng2.12887 VL - 6 IS - 11 SP - 1 EP - 22 PB - John Wiley & Sons Ltd. CY - Berlin AN - OPUS4-59800 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Diercks, Philipp A1 - Gläser, Dennis A1 - Lünsdorf, Ontje A1 - Selzer, Michael A1 - Flemisch, Bernd A1 - Unger, Jörg F. T1 - Evaluation of tools for describing, reproducing and reusing scientific workflows N2 - In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust. In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSs were deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https://github.com/BAMresearch/NFDI4IngScientificWorkflowRequirements), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements. KW - FAIR KW - Reproducibility KW - Scientific workflows KW - Tool comparison KW - Workflow management PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584832 DO - https://doi.org/10.48694/inggrid.3726 VL - 1 IS - 1 SP - 1 EP - 27 AN - OPUS4-58483 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Agrawal, A. A1 - Tamsen, E. A1 - Unger, Jörg F. A1 - Koutsourelakis, P-S T1 - From concrete mixture to structural design—a holistic optimization procedure in the presence of uncertainties N2 - We propose a systematic design approach for the precast concrete industry to promote sustainable construction practices. By employing a holistic optimization procedure, we combine the concrete mixture design and structural simulations in a joint, forward workflow that we ultimately seek to invert. In this manner, new mixtures beyond standard ranges can be considered. Any design effort should account for the presence of uncertainties which can be aleatoric or epistemic as when data are used to calibrate physical models or identify models that fill missing links in the workflow. Inverting the causal relations established poses several challenges especially when these involve physicsbased models which more often than not, do not provide derivatives/sensitivities or when design constraints are present. To this end, we advocate Variational Optimization, with proposed extensions and appropriately chosen heuristics to overcome the aforementioned challenges. The proposed approach to treat the design process as a workflow, learn the missing links from data/models, and finally perform global optimization using the workflow is transferable to several other materials, structural, and mechanical problems. In the present work, the efficacy of the method is exemplarily illustrated using the design of a precast concrete beam with the objective to minimize the global warming potential while satisfying a number of constraints associated with its load-bearing capacity after 28 days according to the Eurocode, the demolding time as computed by a complex nonlinear finite element model, and the maximum temperature during the hydration. KW - Black-box optimization under uncertainty KW - Mix design KW - Performance oriented design KW - Precast concrete KW - Probabilistic machine learning KW - Sustainable material design PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615443 DO - https://doi.org/10.1017/dce.2024.18 VL - 5 IS - e20 SP - 1 EP - 32 PB - Cambridge University Press CY - England AN - OPUS4-61544 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Saif-Ur-Rehman, A1 - Wolf, Christoph A1 - Kujath, Cezary A1 - He, Yuxiang A1 - Mezhof, Alexander A1 - Hüsken, Götz A1 - Jeyifous, Olubunmi Anthony A1 - Unger, Jörg F. ED - Rogge, Andreas ED - Meng, Birgit T1 - Modellierung und Simulation von 3D Betondruck – Design- und Prozessoptimierung N2 - Der 3D Betondruck ermöglicht die effiziente und ressourcenschonendere Herstellung maßgeschneiderter Betonstrukturen. Trotz des großen Potentials gibt es bisher keine einheitlichen Normen und Standards für den sicheren und robusten 3D Betondruck. Der Beitrag zeigt das Potential numerischer Methoden und Modelle zur effizienten und ressourcenschonenden Design- und Prozessoptimierung des 3D Betondrucks. Dazu werden die aktuellen Forschungsarbeiten im Bereich der Entwicklung robuster und zuverlässiger numerischer Zwillinge, geeigneter Optimierungstools sowie digitaler Workflows diskutiert. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Modellierung KW - Simulation KW - 3D Betondruck KW - Designoptimierung KW - Prozessoptimierung PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613023 SN - 978-3-9818564-7-7 SP - 132 EP - 137 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61302 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Andrés Arcones, Daniel A1 - Unger, Jörg F. ED - Rogge, Andreas ED - Meng, Birgit 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 - Modellunsicherheit KW - Parameterunsicherheit KW - Digitaler Brückenzwilling PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613013 SN - 978-3-9818564-7-7 SP - 218 EP - 223 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61301 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - Unger, Jörg F. T1 - From advanced simulation models to industrial applications N2 - The integration of advanced simulation models into industrial applications, especially in civil engineering, is a challenge that requires to develop new concepts. In particular, this is related to the validation of models using open access and FAIR data as well as objective methods for identifying model parameters and model comparison with the integration of both the models and their calibration into reproducible workflow systems. The challenges are illustratively discussed for a variety of applications ranging from bridge monitoring over the structural behavior under blast loading up to the additive manufacturing of concrete. An important basis for model validation is the existence of machine-readable data structures for experimental data, e.g. based on semantic representations. In addition, platforms to share this structured data in a searchable format in addition with reproducible scientific workflows must be established. Based on these tools, objective methods for model parameter estimation and validation of the models are a critical step in ensuring their accuracy and usefulness in industrial applications. Bayesian inference is one approach that can be used to optimize simulation parameters, improving the accuracy of the model while being able to estimate the uncertainty. Integrating these procedures in a real-time application with a bidirectional information exchange between the physical systems and the virtual simulation model leads to the concept of digital twins. For complex simulation models, the computational efficiency is a significant barrier both for real-time applications and in the context of model calibration with potentially millions of required forward-model evaluations. As a consequence, reduced order modeling approaches such as the proper generalized decomposition method can be used to build an efficient to evaluate model - speeding up the online computation but requiring a precomputation in an offline phase. Finally, a perspective for integrating material and structural design into a joint design optimization is given. The integration of advanced simulation models into industrial applications is a rapidly evolving field with significant potential for innovation and progress. From additive manufacturing of concrete to digital twins and reduced order modeling, there are many tools and approaches that can be used to improve industrial processes and drive progress in a variety of industries. T2 - Kolloquium des MUSEN-Zentrums Braunschweig CY - Braunschweig, Germany DA - 04.05.2023 KW - Digital twins KW - Reduced order models PY - 2023 AN - OPUS4-60842 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Efficient structural reliability analysis by using a PGD model in an adaptive importance sampling schema N2 - One of the most important goals in civil engineering is to guarantee the safety of the construction. Standards prescribe a required failure probability in the order of 10−4 to 10−6. Generally, it is not possible to compute the failure probability analytically. Therefore, many approximation methods have been developed to estimate the failure probability. Nevertheless, these methods still require a large number of evaluations of the investigated structure, usually finite element (FE) simulations, making full probabilistic design studies not feasible for relevant applications. The aim of this paper is to increase the efficiency of structural reliability analysis by means of reduced order models. The developed method paves the way for using full probabilistic approaches in industrial applications. In the proposed PGD reliability analysis, the solution of the structural computation is directly obtained from evaluating the PGD solution for a specific parameter set without computing a full FE simulation. Additionally, an adaptive importance sampling scheme is used to minimize the total number of required samples. The accuracy of the failure probability depends on the accuracy of the PGD model (mainly influenced on mesh discretization and mode truncation) as well as the number of samples in the sampling algorithm. Therefore, a general iterative PGD reliability procedure is developed to automatically verify the accuracy of the computed failure probability. It is based on a goal-oriented refinement of the PGD model around the adaptively approximated design point. The methodology is applied and evaluated for 1D and 2D examples. The computational savings compared to the method based on a FE model is shown and the influence of the accuracy of the PGD model on the failure probability is studied. KW - Reliability KW - Probability of failure KW - Adaptive importance sampling KW - Reduced order models KW - Proper Generalized Decomposition PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-510345 DO - https://doi.org/10.1186/s40323-020-00168-z VL - 7 SP - Article number: 29 PB - SpringerOpen AN - OPUS4-51034 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Villani, P. A1 - Unger, Jörg F. A1 - Weiser, M. T1 - Adaptive Gaussian process regression for Bayesian inverse problems N2 - We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems. T2 - Algoritmy 2024 - Central-European Conference on Scientific Computing CY - Vysoké Tatry, Slovakia DA - 15.03.2024 KW - Active learning KW - Bayesian inverse problems KW - Gaussian Process Regression (GPR) KW - Parameter identification KW - Surrogate models PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600408 DO - https://doi.org/10.48550/arXiv.2404.19459 SN - 2331-8422 SP - 1 EP - 12 PB - Cornell University CY - Ithaca, NY AN - OPUS4-60040 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Tamsen, Erik A1 - Agrawal, A. A1 - Koutsourelakis, P.-S. ED - Rogge, Andreas ED - Meng, Birgit T1 - Von Messdaten zum optimierten Bauteil durch Kombination von Material- und Strukturdesign N2 - Die Entwicklung eines optimierten Designs für Bauwerke erfordert die Berücksichtigung sowohl des Materials als auch des Strukturdesigns. Ziel des Beitrages ist die Vorstellung eines Designprinzips basierend auf automatisierten Workflows, das am Beispiel eines Biegebalkendesign zur Reduzierung des Treibhauspotenzials vorgestellt wird. Eine ganzheitliche Optimierung berücksichtigt Material- und Strukturdesign. Es werden physikalische Modelle mit Ansätzen aus dem maschinellen Lernen kombiniert, die mit experimentellen Daten kalibriert bzw. trainiert werden. Eine besondere Bedeutung hat dabei die Berücksichtigung von Unsicherheiten. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Materialdesign KW - Strukturdesign PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613003 SN - 978-3-9818564-7-7 SP - 209 EP - 217 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61300 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rosenbusch, Sjard Mathis A1 - Balzani, D. A1 - Unger, Jörg F. T1 - Regularization of softening plasticity models for explicit dynamics using a gradient-enhanced modified Johnson–Holmquist model N2 - The behavior of concrete under high strain rates is often described by plasticity models with softening, which is modeled by a reduction of the yield surface as a function of the local equivalent plastic strain. Many of these models are local and therefore produce mesh-dependent results. In this contribution, the gradient-enhancement of such models is investigated to mitigate the mesh-dependency. First, the mesh-dependency of these local formulations based on the analysis with a modified JH2 model as a representative for these constitutive formulations is demonstrated using a one-dimensional benchmark example. In the benchmark, the width of the damaged zone decreases upon mesh-refinement and the dissipated plastic energy tends to zero. It is further shown that a significantly small safety factor for the critical time step is needed in order to achieve accurate results for the benchmark example. The first investigated gradient-enhancement approach replaces the equivalent local plastic strain with its nonlocal counterpart. The enhancement is based on the inclusion of inertia and damping to the additional Helmholtz equation which enables the use of the central difference method as an explicit solver. This method successfully distributes the damage over several elements, however, the local equivalent plastic strain still localizes into one cell. The inclusion of hardening with respect to the local equivalent plastic strain inhibits the localization and the dissipated plastic energy converges with mesh-refinement. This is further confirmed in a two-dimensional wedge-splitting experiment and a four-point bending test where the damage pattern produced by the local model is mesh-dependent as well and the dissipated plastic energy tends to zero with mesh-refinement. The proposed nonlocal model with hardening results in a consistent damage pattern and the dissipated plastic energy converges. Furthermore, the nonlocal model with hardening is less sensitive to time step refinement, such that computational efficiency can be improved compared to the local model. The numerical experiments are implemented using the free open source tool FEniCSx and have been made available on Zenodo. KW - Gradient plasticity KW - Explicit dynamics KW - JH2 model KW - Concrete modeling KW - FEniCS KW - Mesh convergence KW - Time step convergence PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630453 DO - https://doi.org/10.1016/j.ijimpeng.2024.105209 SN - 1879-3509 VL - 198 SP - 1 EP - 18 PB - Elsevier Ltd. CY - Schweiz AN - OPUS4-63045 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rosenbusch, Sjard Mathis A1 - Diercks, Philipp A1 - Kindrachuk, Vitaliy A1 - Unger, Jörg F. T1 - Integrating custom constitutive models into FEniCSx: A versatile approach and case studies N2 - The development and integration of user-defined constitutive relationships into finite element (FE) tools using standardized interfaces play a pivotal role in advancing the capabilities of FE solvers for structural mechanics applications. While commercial FE solvers like Abaqus and Ansys have designed their interfaces to provide custom stresses, tangents, and updated history variables, the open-source solver FEniCSx remains efficient only when the constitutive update has an analytical representation. This restricts the application of FEniCSx for non-linear structural mechanics. Since FEniCSx has become a powerful and popular open-source tool for solving partial differential equations, particularly due to its automatic computation of Hessians, we aim to develop a generalized interface to enhance its capability for constitutive modeling. This approach will address complex constitutive equations that require iterative solutions at the quadrature point level. Specific implementation challenges, such as using return-mapping procedures, can then be managed commonly. The provided interface for custom constitutive models offers a versatile way to implement them in various languages, including C++, Python, Rust, and Fortran. Finally, with UMATs for viscoplastic models as an example, we demonstrate how existing user subroutines can be incorporated into the interface and utilized within the FEniCSx framework. KW - Finite element method KW - Constitutive models KW - FEniCSx KW - UMAT KW - Rust KW - Python KW - C++ PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630439 DO - https://doi.org/10.1016/j.advengsoft.2025.103922 SN - 0965-9978 VL - 206 SP - 1 EP - 11 PB - Elsevier CY - Amsterdam AN - OPUS4-63043 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 - Hirthammer, Volker A1 - Unger, Jörg F. A1 - Ozbolt, J. ED - von Scheven, M. ED - Keip, M.-A. ED - Karajan, N. T1 - Mesoscale influence on the macroscopic material behavior of concrete N2 - The heterogeneous mesostructure of concreted causes local stress concentrations. Stress dependent phenomena like damage and creep as well as their interactions are effected by those stress concentrations. Therefore a material model’s macroscopic behavior will differ whether the mesoscale structure is considered or not. The differences between the mesoscale approach and an homogeneous approach will be presented. The results are discussed with focus on the true materials behavior. T2 - 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry CY - Stuttgart, Germany DA - 11.10.2017 KW - Mesoscale KW - Material behavior KW - Macroscopic PY - 2017 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-436611 DO - https://doi.org/10.18419/opus-9334 VL - 7 SP - MS13, 414 EP - 417 PB - Institute for Structural Mechanics, University of Stuttgart CY - Stuttgart AN - OPUS4-43661 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pohl, Christoph A1 - Unger, Jörg F. ED - von Scheven, M. ED - Keip, M.-A. ED - Karajan, N. T1 - Obtaining macroscopic properties from a mesoscale thermomechanical model of concrete N2 - A coupled thermomechanical mesoscale model for concrete under heating is presented. When considering the heterogeneous structure under coupled loads, complex macroscopic material properties can be modelled using simple constitutive relations. For instance, damage evolution is directly driven by the incompatibility of thermal strains between matrix and aggregates. Without prescribing fc = f(T), a decline in compressive strength with rising temperatures will be shown. T2 - 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry CY - Stuttgart, Germany DA - 11.10.2017 KW - Multiscale KW - Concrete mesoscale KW - Thermomechanical PY - 2017 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-436540 DO - https://doi.org/10.18419/opus-9334 VL - 7 SP - MS13, 418 EP - 422 PB - Institute for Structural Mechanics, University of Stuttgart CY - Stuttgart AN - OPUS4-43654 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Huschke, Philip A1 - Unger, Jörg F. ED - von Scheven, M. ED - Keip, M.-A. ED - Karajan, N. T1 - Domain decomposition methods for fracture mechanics problems and its application to fiber reinforced concrete N2 - A finite element tearing and interconnecting (FETI) approach for phase-field models and Gradient enhanced damage models is presented. These diffusive crack models can solve fracture mechanics problems by integrating a set of partial differential equations and thus avoid the explicit treatment of discontinuities. However, they require a fine discretization in the vicinity of the crack. FETI methods distribute the computational cost among multiple processors and thus speed up the computation. T2 - 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry CY - Stuttgart, Germany DA - 11.10.2017 KW - FETI KW - Domain decomposition methods KW - Fracture mechanics problems PY - 2017 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-436764 DO - https://doi.org/10.18419/opus-9334 VL - 7 SP - 444 EP - 448 PB - Institute for Structural Mechanics, University of Stuttgart CY - Stuttgart AN - OPUS4-43676 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Titscher, Thomas A1 - Unger, Jörg F. A1 - Oliver, J. ED - von Scheven, M. ED - Keip, M.-A. ED - Karajan, N. T1 - Cycle-by-cycle fatigue damage model for concrete N2 - Damage caused by stress concentrations in the complex mesoscopic geometry of concrete leads to continuous stress redistribution over the material’s life time. The presented fatigue damage model captures this by resolving each load cycle in a cycle-by-cycle time integration. The model extends a static damage model to failure caused by the (time dependent) strain amplitudes and, thus, allows calibrating the majority of the material’s parameters in static experiments. T2 - 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry CY - Stuttgart, Germany DA - 11.10.2017 KW - Cycle-by-cycle KW - Fatigue damage KW - Mesoscopic PY - 2017 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-436681 DO - https://doi.org/10.18419/opus-9334 VL - 7 SP - MS13, 440 EP - 443 PB - Institute for Structural Mechanics, University of Stuttgart CY - Stuttgart AN - OPUS4-43668 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Coelho Lima, Isabela A1 - Robens-Radermacher, Annika A1 - Titscher, Thomas A1 - Kadoke, Daniel A1 - Koutsourelakis, P.-S. A1 - Unger, Jörg F. T1 - Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forwardmodel's accuracy N2 - Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results. KW - Variational inference KW - Proper generalized decomposition KW - Goal-oriented KW - Digital twin KW - Random field PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-555755 DO - https://doi.org/10.1007/s00466-022-02214-6 SN - 1432-0924 SP - 1 EP - 22 PB - Springer CY - Berlin AN - OPUS4-55575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pohl, Christoph A1 - Smilauer, V. A1 - Unger, Jörg F. T1 - A three-phase transport model for high-temperature concrete simulations validated with X-ray CT data N2 - Concrete exposure to high temperatures induces thermo-hygral phenomena, causing water phase changes, buildup of pore pressure and vulnerability to spalling. In order to predict these phenomena under various conditions, a three-phase transport model is proposed. The model is validated on X-ray CT data up to 320 ◦C, showing good agreement of the temperature profiles and moisture changes. A dehydration description, traditionally derived from thermogravimetric analysis, was replaced by a formulation based on data from neutron radiography. In addition, treating porosity and dehydration evolution as independent processes, previous approaches do not fulfil the solid mass balance. As a consequence, a new formulation is proposed that introduces the porosity as an independent variable, ensuring the latter condition. KW - Concrete KW - Porous media KW - Spalling KW - Dehydration KW - Moisture transport KW - Heat transfer KW - Pore pressure KW - Porosity KW - Finite elements PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-532840 UR - https://doi.org/10.5281/zenodo.4890635 DO - https://doi.org/10.3390/ma14175047 SN - 1996-1944 VL - 14 IS - 17 SP - 1 EP - 21 PB - MDPI CY - Basel AN - OPUS4-53284 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Concrete under cyclic loading a continuum damage model and a temporal multiscale approach N2 - The durability of concrete structures and its performance over the lifetime is strongly influenced by many interacting phenomena such as e.g. mechanical degradation due to fatigue loading, loss of prestress, degradation due to chemical reactions or creep and shrinkage. Failure due to cyclic loading is generally not instantaneous, but characterized by a steady damage accumulation. Many constitutive models for concrete are currently available, which are applicable for specific loading regimes, different time scales and different resolution scales. A key limitation is that the models often do not address issues related to fatigue on a structural level. Very few models can be found in the literature that reproduce deterioration of concrete under repeated loading-unloading cycles. The objective of this paper is the presentation of numerical methods for the simulation of concrete under fatigue loading using a temporal multiscale method. First, a continuum damage model for concrete is developed with a focus on fatigue under compressive stresses. This includes the possibility to model stress redistributions and capture size effects. In contrast to cycle based approaches, where damage is accumulated based on the number of full stress cycles, a strain based approach is developed that can capture cyclic degradation under variable loading cycles including different amplitudes and loading frequencies. Second, a multiscale approach in time is presented to enable structural computations of fatigue failure with a reduced computational effort. The damage rate within the short time scale corresponding to a single cycle is computed based on a Fourier based approach. This evolution equation is then solved on the long time scale using different time integration schemes. T2 - 6th European Conference on Computational Mechanics (ECCM 6) CY - Glasgow, UK DA - 11.06.2018 KW - Cycle jump KW - Fatigue damage KW - Concrete PY - 2018 AN - OPUS4-45696 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Coelho Lima, Isabela A1 - Unger, Jörg F. T1 - Variational Bayesian Inference for structural model update N2 - Appropriate monitoring of transportation infrastructures (e.g. bridges) is of utmost importance to ensure safe operation conditions. Accurate and reliable assessment of such structures can be achieved through the integration of data from non-destructive testing, advanced modeling and model updating techniques. The Bayesian framework has been widely used for updating engineering and mechanical models, due to its probabilistic description of information, in which the posterior probability distribution reflects the knowledge, over the model parameters of interest, inferred from the data. For most real-life applications, the computation of the true posterior involves integrals that are analytically intractable, therefore the implementation of Bayesian inference requires in practice some approximation methods. This paper investigates the application of Variational Bayesian Inference for structural model parameter identification and update, based on measurements from a real experimental setup. The Variational Bayesian method circumvents the issue of evaluating intractable integrals by using a factorized approximation of the true posterior (mean field approximation) and by choosing a family of conjugate distributions that facilitates the calculations. Inference in the Variational Bayesian framework is seen as solving an optimization problem with the aim of finding the parameters of the factorized posterior which would minimize its Kullback-Leibler divergence in relation to the exact posterior. The Variational Approach is an efficient alternative to sampling methods, such as Markov Chain Monte Carlo, since the latter’s accuracy depends on sampling from the posterior distribution a sufficient amount of times (and therefore requiring an equivalent number of computations of the forward problem, which can be quite expensive). T2 - Data Science Summer School CY - École polytechnique Université Paris Saclay, France DA - 25.06.2018 KW - Variational Bayesian KW - Structural monitoring KW - Bayesian inference PY - 2018 AN - OPUS4-45604 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Multiscale simulation of localization phenomena using XFEM² T2 - ECCOMAS 2012 CY - Vienna, Austria DA - 2012-09-10 PY - 2012 AN - OPUS4-26532 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Otto, Peter A1 - De Lorenzis, L. A1 - Unger, Jörg F. T1 - A regularized model for impact in explicit dynamics applied to the split Hopkinson pressure bar N2 - In the numerical simulation of Impact phenomena, artificial oscillations can occur due to an instantaneous change of velocity in the contact area. In this paper, a nonlinear penalty regularization is used to avoid these oscillations. Aparticular focus is the investigation of higher order methods in space and time to increase the computational efficiency. The spatial discretization is realized by higher order spectral element methods that are characterized by a diagonal mass matrix. The time integration scheme is based on half-explicit Runge–Kutta scheme of fourth order. For the conditionally stable scheme, the critical time step is influenced by the penalty regularization. A framework is presented to adjust the penalty stiffness and the time step for a specific mesh to avoid oscillations. The methods presented in this paper are applied to 1D-simulations of a split Hopkinson pressure bar, which is commonly used for the investigation of materials under dynamic loading. KW - Penalty regularization KW - Spectral element method KW - Differential–algebraic equations KW - Split Hopkinson pressure bar KW - Dynamic contact PY - 2016 DO - https://doi.org/10.1007/s00466-016-1311-1 SP - 1 EP - 15 PB - Springer-Verlag CY - Berlin Heidelberg AN - OPUS4-36967 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Hierarchical multiscale models for localization phenomena within the framework of FE2-X1 T2 - Computational Modelling of Concrete and Concrete Structures CY - St. Anton am Arlberg, Austria DA - 2014-03-24 PY - 2014 AN - OPUS4-32902 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. ED - Idelsohn, S. ED - Sonzogni, V. ED - Coutinho, A. ED - Cruchaga, M. ED - Lew, A. ED - Cerrolaza, M. T1 - Numerical simulation of ultrasonic wave propagation using higher order methods in space and time N2 - The paper discusses the efficient simulation of ultrasonic wave propagation. It is demonstrated that a combination of higher methods in space and time leads to a significant performance boost. Higher order spectral elements are used for the spatial discretization. A comparison with standard finite elements shows the advantages when using explicit time integration schemes. For the temporal discretization, an efficient explicit fourth order Nyström method is presented. Its computational efficiency for wave propagation problems is compared to a second order Velocity Verlet integration. T2 - 1st Pan-American Congress on Computational Mechanics - PANACM 2015 CY - Buenos Aires, Argentina DA - 27.04.2015 KW - Elastic wave propagation KW - Spectral element method KW - Nyström methods PY - 2015 SN - 978-84-943928-2-5 SP - 1041 EP - 1052 PB - International Center for Numerical Methods in Engineering (CIMNE) CY - Barcelona, Spain AN - OPUS4-38645 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Numerical simulation of ultrasonic wave propagation using higher order methods in space and time N2 - The paper discusses the efficient simulation of ultrasonic wave propagation. It is demonstrated that a combination of higher methods in space and time leads to a significant performance boost. Higher order spectral elements are used for the spatial discretization. A comparison with standard finite elements shows the advantages when using explicit time integration schemes. For the temporal discretization, an efficient explicit fourth order Nyström method is presented. Its computational efficiency for wave propagation problems is compared to a second order Velocity Verlet integration. T2 - 1st Pan-American Congress on Computational Mechanics - PANACM 2015 CY - Buenos Aires, Argentina DA - 27.04.2015 KW - Elastic wave propagation KW - Spectral element method KW - Nyström methods PY - 2015 AN - OPUS4-38646 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Unger, Jörg F. A1 - Kindrachuk, Vitaliy T1 - A Fourier transformation-based temporal integration scheme for viscoplastic solids subjected to fatigue deterioration N2 - Using continuum damage mechanics (CDM) for lifetime prediction requires numerical integration of evolving damage until the onset of failure. The primary challenge for the simulation of structural fatigue failure is caused by the enormous computational costs due to cycle-by-cycle temporal integration throughout the whole loading history, which is in the order of 103–107 cycles. As a consequence, most approaches circumvent this problem and use empirical methods such as Wöhler curves. They are well suited for approximating the lifetime, but they are not capable to capture a realistic degradation of the material including redistribution of stresses. The main objective of the paper is to provide a technique for finite element (FE) simulations of structures under fatigue loading while reducing computational costs. A Fourier transformation-based temporal integration (FTTI) scheme is proposed, which adapts the conventional FE method for modeling the viscoplastic deterioration in a structure subjected to cyclic loading. The response fields are represented by a Fourier series which assumes a temporal scale separation: a microchronological (short time) scale arises from the oscillatory loading and a macrochronological (long time) scale is due to the slow material relaxation resulting from yielding and damage evolution. The original dynamic boundary value problem (BVP) is approximated by the stationary BVP on the microchronological scale. Alternation of the displacement field on the macrochronological scale is correlated with evolution of the history variables by means of a high order adaptive cycle jump method. Performance and significant acceleration of the FE simulations is demonstrated at different loading scenarios for a constitutive damage model where the progressive damage accumulation is driven by viscoplastic yielding. KW - Fatigue KW - Accelerated time integration KW - Continuum damage mechanics KW - Fourier series PY - 2017 DO - https://doi.org/10.1016/j.ijfatigue.2017.03.015 SN - 0142-1123 SN - 1879-3452 VL - 100 IS - 1 SP - 215 EP - 228 PB - Elsevier Ltd. AN - OPUS4-39616 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Kindrachuk, Vitaliy A1 - Hirthammer, Volker A1 - Titscher, Thomas A1 - Pohl, Christoph T1 - The importance of multiphysics and multiscale modelling of concrete to understand its complex macroscopic properties N2 - Concrete is a complex material. Its properties evolve over time, especially at early age, and are dependent on environmental conditions, i.e. temperature and moisture conditions, as well as the composition of the material. This leads to a variety of macroscopic phenomena such as hydration/solidification/hardening, creep and shrinkage, thermal strains, damage and inelastic deformations. Most of these phenomena are characterized by specific set of model assumptions and often an additive decomposition of strains into elastic, plastic, shrinkage and creep components is performed. Each of these phenomena are investigated separately and a number of respective independent models have been designed. The interactions are then accounted for by adding appropriate correction factors or additional models for the particular interaction. This paper discusses the importance of reconsider even in the experimental phase the model assumptions required to generalize the experimental data into models used in design codes. It is especially underlined that the complex macroscopic behaviour of concrete is strongly influenced by its multiscale and multiphyscis nature and two examples (shrinkage and fatigue) of interacting phenomena are discussed. T2 - International RILEM Conference on Materials, Systems and Structures in Civil Engineering CY - Lyngby, Denmark DA - 22.08.2016 KW - Concrete KW - Multiscale KW - Multiphysics PY - 2016 VL - 1 SP - 115 EP - 124 PB - International RILEM Conference on Materials, Systems and Structures in Civil Engineering, Conference segment on COST TU1404 AN - OPUS4-38651 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - The importance of multiphysics and multiscale modeling of concrete to understand its complex macroscopic properties N2 - Concrete is a complex material. Its properties evolve over time, especially at early age, and are dependent on environmental conditions, i.e. temperature and moisture conditions, as well as the composition of the material. This leads to a variety of macroscopic phenomena such as hydration/solidification/hardening, creep and shrinkage, thermal strains, damage and inelastic deformations. Most of these phenomena are characterized by specific set of model assumptions and often an additive decomposition of strains into elastic, plastic, shrinkage and creep components is performed. Each of these phenomena are investigated separately and a number of respective independent models have been designed. The interactions are then accounted for by adding appropriate correction factors or additional models for the particular interaction. This paper discusses the importance of reconsider even in the experimental phase the model assumptions required to generalize the experimental data into models used in design codes. It is especially underlined that the complex macroscopic behaviour of concrete is strongly influenced by its multiscale and multiphyscis nature and two examples (shrinkage and fatigue) of interacting phenomena are discussed. T2 - International RILEM Conference on Materials, Systems and Structures in Civil Engineering CY - Lyngby, Denmark DA - 22.08.2016 KW - Multiscale KW - Concrete KW - Multiphysics PY - 2016 AN - OPUS4-38652 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Multiscale modeling of concrete - from mesoscale to macroscale N2 - Concrete is one of the most attractive building materials consumed by humans more than any other material, except water. The particular importance of concrete for a sustainable, energy-efficient economy is highlighted by the fact that about 5% of worldwide CO2 emissions are created from the cement industry. Concrete is a very complex material. Its properties are time dependent, which includes the solidification after casting or creep and shrinkage. In addition, concrete is a quasi-brittle material which requires to model the softening behavior including the challenge of appropriate regularization strategies. Many characteristic features are strongly related to its complex heterogeneous structure, including particles and mortar on the mesoscale or the CSH-phases on the micro scale. At first, a short introduction to the generation of mesoscale geometries as a three phase composite including particles, mortar matrix and the interfacial transition zone is given. Afterwards, the numerical model including meshing (XFEM and aligned meshes) as well as regularized material models for the mortar phase are presented. The focus of the presentation is the discussion of multiscale approaches to combine mesoscale models with realistic macroscale models. This includes a concurrent approach using an adaptive transition between mesoscale and macroscale models which are coupled using the mortar method. A second hierarchical approach is based on the concept of FE², which is extended to incorporate softening by solving a fine scale boundary value problem for each macroscopic integration point. T2 - COST Action TU 1404 – 2nd WORKSHOP CY - Wien, Austria DA - 19.09.2015 KW - Multiscale KW - Localization KW - Damage KW - FE² KW - Mesoscale PY - 2015 AN - OPUS4-38650 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Mechanics group at Federal Institute for Materials Research and Testing N2 - The poster presents the work of the mechanics group in BAM, department 7. The current projects deal with the simulation of concrete on different spatial and temporal scales - ranging from the creation of mesoscale geometries up to fatigue and high strain rate impact phenomena. T2 - COST Meeting 1404 CY - Ljubljana, Slovenia DA - 16.05.2015 KW - Mechanics KW - Contact KW - Multiscale KW - Concrete KW - Constitutive modelling PY - 2015 AN - OPUS4-38647 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Unger, Jörg F. T1 - An FE²-X¹ approach for multiscale localization phenomena N2 - In this paper, a new methodology based on the Hill–Mandel lemma in an FE² sense is proposed that is able to deal with localized deformations. This is achieved by decomposing the displacement field of the fine scale model into a homogeneous part, fluctuations, and a cracking part based on additional degrees of freedom (X¹)—the crack opening in normal and tangential directions. Based on this decomposition, the Hill–Mandel lemma is extended to relate coarse and fine scale energies using the assumption of separation of scales such that the fine scale model is not required to have the same size as the corresponding macroscopic integration point. In addition, a procedure is introduced to mimic periodic boundary conditions in the linear elastic range by adding additional shape functions for the boundary nodes that represent the difference between periodic boundary conditions and pure displacement boundary conditions due to the same macroscopic strain. In order to decrease the computational effort, an adaptive strategy is proposed allowing different macroscopic integration points to be resolved in different levels on the fine scale. KW - Fracture KW - Multiscale KW - Finite elements KW - Constitutive behavior KW - Inhomogeneous material PY - 2013 DO - https://doi.org/10.1016/j.jmps.2012.12.010 SN - 0022-5096 VL - 61 IS - 4 SP - 928 EP - 948 PB - Elsevier AN - OPUS4-35902 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - The benefit of mesoscale models for concrete to understand its complex macroscopic behavior N2 - Concrete is one of the most important building materials world wide. The safety of constructions build from concrete is of utmost importance in daily life. As a consequence, accurate predictions of the structural behavior over the entire lifetime of concrete structures are required to ensure a prescribed safety level. A lack of exact models and/or stochastically varying constitutive parameters are compensated by large safety factors. The nonlinear structural performance is strongly related to the constitutive behavior of concrete. Arbitrary complex models can be used to describe the macroscopic constitutive behavior of concrete. The parameters in these models often lack any physical meaning. Consequently, the fitting can only be performed by an inverse analysis. In contrast, models on finer scales are able to simulate the physical phenomena more accurately and are thus better suited to understand the failure mechanisms. In addition, the macroscopically observed strong nonlinearities can at least partially be explained by the direct modeling of the material heterogeneities on finer scales. The presentation discusses several phenomena that are strongly related to the internal microstructure of concrete. This includes the discrepancy between the unique results of a numerical model and the stochastic scatter observed in real experiments. A short discussion on the generation of random mesoscale geometries to model aggregates and mortar matrix explicitly and random fields are given. The strong nonlinearities especially for stresses close to the peak strength are usually the result of failure in the mortar matrix or the interfacial transition zone, whereas the aggregates are inert and often can accurately be modeled by a linear elastic model. The different constitutive properties lead to eigenstresses that strongly in uence the macroscopic behavior. In addition, this effect is even more pronounced when dealing with multiphysics phenomena such as drying, creep and shrinkage, fatigue or thermal problems. It will be demonstrated for several examples that simple models on the fine scale can be superimposed and coupled to obtain a macroscopically nonlinear behavior, where the superposition principle does not hold any longer. Finally, a short discussion on upscaling techniques to couple mesoscale models with large scale structural problems is given. T2 - ECCOMAS 2016 CY - Kreta, Greece DA - 05.06.2016 KW - FEM, concrete, mesoscale PY - 2016 AN - OPUS4-36459 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zorn, Sebastian A1 - Unger, Jörg F. A1 - Schneider, Ronald ED - Silva Gomes, J. F. ED - Meguid, S. A. T1 - A probabilistic method for identification of vehicle loads and system parameters for reinforced concrete bridges N2 - Growth of vehicle traffic density can be observed in many countries all over the world. This accretion is caused by world-wide population growth on the one hand, but also by increasing freight volumes and, thus, freight transports on the streets on the other hand. This increased exposure becomes more and more of a problem for the civil infrastructure such as bridges. Many of these bridges are nowadays stressed to their limits by higher loads than they were originally designed for and/or operating times beyond the initially predicted life span. This raises questions about structural safety and lifetime prediction, of course, and therefore illustrates the need for accurate structural monitoring. Since the lifetime of bridge structures is primarily influenced by their traffic loading, an accurate identification of load configurations over the whole length of a structure is most desirable. In this paper, a method for vehicle load identification is proposed. It involves Bayesian Analysis and (quasi-)static importance functions in order to estimate vehicle positions, velocities and weights. The structure is modeled with finite elements in order to generate model predictions for different load configurations. The model predictions are compared to the actual measured data to identify the most probable loading configuration for that measurement. This involves the use of enhanced Monte Carlo simulations such as MCMC to reduce the computational effort. The measured data from different kinds of sensors can (and should) be combined for accuracy gain – in this case a combination of measured displacements and inclinations. Since the measurements take place over some time during the passage of the vehicle, these estimations are carried out for several time instants for which the estimation is carried out. The advantage of using Bayesian Updating Method is the embodied learning effect leading to an improvement of the estimation when adding new information in a new calculation step. Using the estimates for the loading conditions of a bridge structure as well as measurements of the structural responses, Bayesian analysis is again used in order to estimate localized structural parameters such as Young's modulus or Moments of Inertia in form of probability density functions yielding most probable values for the parameters. Considering the difficulties for load identification close to the support poles of the bridge and therefore for the proposed structural parameter identification procedure, it is clear that this problem is ill posed. Bayesian regularization methods also have proven to be very effective when handling ill posed problems. T2 - IRF2016 - Integrity Reliability Failure CY - Porto, Portugal DA - 24.07.2016 KW - Vehicle load identification KW - Structural health monitoring KW - Bayesian updating method PY - 2016 SN - 978-989-98832-4-6 VL - 2016 SP - PAPER REF: 6294-575 EP - 576 PB - INEGI-Instituto de Ciencia e Inovacao em Engenharia Mecanica e Gestao Industrial CY - Porto - Portugal AN - OPUS4-39833 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Coelho Lima, Isabela A1 - Unger, Jörg F. T1 - Variational Bayesian Inference for structural model update N2 - Appropriate monitoring of transportation infrastructures (e.g. bridges) is of utmost importance to ensure safe operation conditions. Accurate and reliable assessment of such structures can be achieved through the integration of data from non-destructive testing, advanced modeling and model updating techniques. The Bayesian framework has been widely used for updating engineering and mechanical models, due to its probabilistic description of information, in which the posterior probability distribution reflects the knowledge, over the model parameters of interest, inferred from the data. For most real-life applications, the computation of the true posterior involves integrals that are analytically intractable, therefore the implementation of Bayesian inference requires in practice some approximation methods. This paper investigates the application of Variational Bayesian Inference for structural model parameter identification and update, based on measurements from a real experimental setup. The Variational Bayesian method circumvents the issue of evaluating intractable integrals by using a factorized approximation of the true posterior (mean field approximation) and by choosing a family of conjugate distributions that facilitates the calculations. Inference in the Variational Bayesian framework is seen as solving an optimization problem with the aim of finding the parameters of the factorized posterior which would minimize its Kullback-Leibler divergence in relation to the exact posterior. The Variational Approach is an efficient alternative to sampling methods, such as Markov Chain Monte Carlo, since the latter’s accuracy depends on sampling from the posterior distribution a sufficient amount of times (and therefore requiring an equivalent number of computations of the forward problem, which can be quite expensive). T2 - ECCM - ECFD 2018 CY - Glasgow, Scotland, UK DA - 11.06.2018 KW - Variational Bayesian KW - Structural monitoring KW - Bayesian inference PY - 2018 AN - OPUS4-45603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Digital Twins for materials and structures N2 - A safe and robust performance is a key criterion when building and maintaining structures and components. Ensuring this criterion at different stages of the lifetime can be supported by applying continuous monitoring concepts. The latter usually can serve multiple purposes, including the determination of material parameters for the design phase, the evaluation of the actual loading/environmental conditions (instead of using conservative estimates that are usually larger) and evaluating or predicting the true performance of the structure (thus decreasing the model bias). In this context, a digital twin of the structure has many benefits. In addition, it allows to introduce virtual sensors to “measure” sensor information that is e.g. inaccessible or unmeasureable. In the limit, the remaining useful life of a structure can be interpreted as a property that can be “measured” indirectly via the numerical model in combination with real sensor data. In order to efficiently use monitoring techniques in the context of a digital twin, it is important to consider the complete chain of information including the choice of sensors, the data processing and structuring, the modelling assumptions, the numerical simulation and finally the stochastic nature of the model prediction. In this presentation, challenges in this context are discussed with a specific focus on Bayesian model updating of the digital twin, accounting for both parameter updates as well as model bias that results from the limitations of modelling assumption. A bottleneck in this approach is the computational effort related to sampling methods such as Markov chain Monte Carlo methods that require many evaluations of the forward model. An alternative to the expensive computation of the forward model for updating the digital twin is the combination with model reduction techniques such as the Proper General Decomposition. The results are illustrated for several examples and scales, ranging from digitals twin for material tests in the lab over lab scale structural digital twins up to damage identification in field experiments. T2 - 1st International Workshop on Metrology for virtual measuring instruments CY - Berlin, Germany DA - 21.09.2021 KW - Digital twin for monitoring KW - Digital Twins for materials and structures PY - 2021 AN - OPUS4-53879 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Diercks, Philipp A1 - Robens-Radermacher, Annika A1 - Rosenbusch, Sjard Mathis A1 - Unger, Jörg F. A1 - Saif-Ur-Rehman, Saif-Ur-Rehman T1 - fenics-constitutive N2 - This software enables the use of nonlinear constitutive models with FEniCSx. KW - Constitutive models KW - FEniCSx KW - Fenics-constitutive PY - 2024 DO - https://doi.org/10.5281/zenodo.13364955 PB - Zenodo CY - Geneva AN - OPUS4-62114 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Rosenbusch, Sjard Mathis A1 - Diercks, Philipp A1 - Kindrachuk, Vitaliy A1 - Unger, Jörg F. T1 - Integrating custom constitutive models into FEniCSx: A versatile approach and case studies N2 - The development and integration of user-defined constitutive relationships into finite element (FE) tools using standardized interfaces play a pivotal role in advancing the capabilities of FE solvers for structural mechanics applications. While commercial FE solvers like Abaqus and Ansys have designed their interfaces to provide custom stresses, tangents, and updated history variables, the open-source solver FEniCSx remains efficient only when the constitutive update has an analytical representation. This restricts the application of FEniCSx for non-linear structural mechanics. Since FEniCSx has become a powerful and popular open-source tool for solving partial differential equations, particularly due to its automatic computation of Hessians, we aim to develop a generalized interface to enhance its capability for constitutive modeling. This approach will address complex constitutive equations that require iterative solutions at the quadrature point level. Specific implementation challenges, such as using return-mapping procedures, can then be managed commonly. The provided interface for custom constitutive models offers a versatile way to implement them in various languages, including C++, Python, Rust, and Fortran. Finally, using the example of UMATs for viscoplastic models, we demonstrate how available user subroutines can be incorporated into the interface while maintaining computational performance of FEniCSx comparable to that of Abaqus. KW - Finite element method KW - Constitutive models KW - FEniCSx KW - UMAT KW - Rust KW - Python KW - C++ PY - 2024 DO - https://doi.org/10.5281/zenodo.13980988 PB - Zenodo CY - Geneva AN - OPUS4-62112 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Jafari, Abbas A1 - Vlachas, K. A1 - Chatzi, E. A1 - Unger, Jörg F. T1 - A Bayesian framework for constitutive model identification via use of full field measurements, with application to heterogeneous materials N2 - In this paper, we present a Bayesian framework for the identification of the parameters of nonlinear constitutive material laws using full-field displacement measurements. The concept of force-based Finite Element Model Updating (FEMU-F) is employed, which relies on the availability of measurable quantities such as displacements and external forces. The proposed approach particularly unfolds the advantage of FEMU-F, as opposed to the conventional FEMU, by directly incorporating information from full-field measured displacements into the model. This feature is well-suited for heterogeneous materials with softening, where the localization zone depends on the random microstructure. Besides, to account for uncertainties in the measured displacements, we treat displacements as additional unknown variables to be identified, alongside the constitutive parameters. A variational Bayesian scheme is then employed to identify these unknowns via approximate posteriors under the assumption of multivariate normal distributions. An optimization problem is then formulated and solved iteratively, aiming to minimize the discrepancy between true and approximate posteriors. The benefit of the proposed approach lies in the stochastic nature of the formulation, which allows to tackle uncertainties related to model parameters and measurement noise. We verify the efficacy of our proposed framework on two simulated examples using gradient damage model with a path-dependent nonlinear constitutive law. Based on a nonlocal equivalent strain norm, this constitutive model can simulate a localized damage zone representing softening and cracking. The first example illustrates an application of the FEMU-F approach to cracked structures including sensitivity studies related to measurement noise and parameters of the prior distributions. In this example, the variational Bayesian solver demonstrates a sizable advantage in terms of computational efficiency compared to a traditional least-square optimizer. The second example demonstrates a sub-domain analysis to tackle challenges associated with limited domain knowledge such as uncertain boundary conditions. KW - Constitutive model identification KW - Full-field measurements KW - Variational Bayes KW - Force-based finite element model updating KW - FEMU-F PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-615476 DO - https://doi.org/10.1016/j.cma.2024.117489 SN - 1879-2138 VL - 433 IS - Part A SP - 1 EP - 32 PB - Elsevier B.V. AN - OPUS4-61547 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Combining material and structural design to reduce the global warming potential of concrete structures N2 - The creation of civil structures such as bridges, dams, and buildings involves a collaborative effort among various experts, each handling specific aspects of the process. Traditionally, this process is performed sequentially, i.e. starting with the structural design with assumptions on minimal material properties, and a subsequent material design within these limits. In the presentation, an alternative procedure is proposed that merges concrete mix design and structural simulations into a unified, progressive workflow. The design procedure is then reformulated as an optimization problem with constraints related to key performance indicators such as e.g. the load bearing capacity. Addressing uncertainties—whether inherent (aleatoric) or arising from incomplete knowledge (epistemic)—is crucial in any design endeavor. These uncertainties may stem from data used to calibrate physical models, from model simplifications or from machine learning models trained with limited amount of data that are used to bridge gaps in the workflow. Inverting the causal relations poses several challenges especially when these physics-based models do not provide derivatives/sensitivities or when design constraints are present. Our approach advocates for Variational Optimization, augmented by proposed extensions and carefully selected heuristics. Integrating the expertise and numerical methods from multiple experts is a challenging task in itself. The presentation discusses the use of workflow tools with a modularization of the computational methods. The presentation includes related challenges in the context of the FAIR principles (Findable, Accessible, Interoperable, Reusable). The design of a precast concrete beam is presented as an example to demonstrate the efficacy of our methodology. The objective is to minimize the environmental impact in terms of the global warming potential while fulfilling constraints related to the load-bearing capacity (Eurocode) as well as the demoulding time and the maximum temperature during hydration.The latter are computed from a macroscopic thermo-mechanical Finite Element simulation that is based on a hydration model on the paste level combined with homogenization procedures to obtain the macroscopic constitutive equations. Experimental data is used to perform a Bayesian calibration of these models. T2 - ECCOMAS CONGRESS 2024 CY - Lisbon, Portugal DA - 03.06.2024 KW - Performance oriented design KW - Black-box optimization under uncertainty KW - Probabilistic machine learning KW - Precast concrete KW - Mix design KW - Sustainable material design PY - 2024 AN - OPUS4-62108 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Uncertainty Quantification and Model Extension for Digital Twins through Model Bias Identification N2 - Using digital twins requires precise digital models that accurately reflect their physical counterparts. Ensuring their trustworthiness and enabling informed decision making requires a robust approach to Uncertainty Quantification (UQ). A suitable model update scheme is crucial to preserve the quality and robustness of simulation-based digital twins. Model bias resulting from discrepancies between computational models and real systems pose a significant challenge in achieving this goal. Two alternative methods for identifying additive model discrepancies are investigated in comparison with the classical Bayesian inference framework - a modularized version of Kennedy and O'Hagan's approach [1] and another based on orthogonal Gaussian processes [2]. Furthermore, embedded bias approaches based on hierarchical Bayesian methods are used to incorporate the bias on the level of the parameters [3]. This improves the predictive quality of quantities that are not directly measured and allows for more accurate estimates of the uncertainty bounds. The approaches are demonstrated for predicting the complete temperature distribution and compensating for thermal deformations in a real structural problem, using data from a limited number of sensors. T2 - FrontUQ 2024 ​ CY - Braunschweig, Germany DA - 24.09.2024 KW - Model bias KW - Uncertainty quantification KW - Digital twin KW - Model updatin PY - 2024 AN - OPUS4-62106 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -