TY - CONF A1 - Jafari, Abbas T1 - Bayesian inference of constitutive law parameters for crack localization using full-field displacement measurements N2 - Physics-based models of mechanical structures are widely adopted for assessing and predicting the behaviour of structures. In the context of structural mechanics, the constitutive law that describes the stress-strain relation forms an important modelling component, which suffers from a considerable amount of uncertainties. These uncertainties primarily arise due to the inherent simplifications and assumptions placed in favor of facilitating the modeling process. Bayesian techniques have been proven to be effective for tackling uncertainties associated with the identification of material model parameters and quantifying the confidence level that can be associated with the placed modeling assumptions. We present a Bayesian framework for the identification of constitutive parameters of quasi-brittle materials suffering strain localization effects, via the use of full-field displacement measurements. The proposed framework explores the idea of force-based Finite Element Model Updating (FEMU-F), which relies on measured full-field displacements and aggregated forces. In particular, the scheme takes advantage of FEMU-F, in contrast to the conventional FEMU, where the information from full-field displacements is directly incorporated into the model. We also address the uncertainties involved in the measured displacements, by treating them as additional unknown variables to be identified, alongside the constitutive parameters. These unknown variables collectively form the inputs to a well-defined objective function, which forms the basis for the Bayesian inference problem. To efficiently solve this Bayesian problem, we employ a variational Bayesian scheme that relies on approximate posteriors represented as multivariate normal distributions. We demonstrate the proposed framework for the parameter identification of a nonlinear path-dependent gradient damage constitutive law, which exhibits strain localization and softening behaviour. The first example illustrates the effectiveness of the inference procedure, highlighting the advantage of FEMU-F in incorporating information about cracks. The second example demonstrates a sub-domain analysis suitable for inferring models with limited domain knowledge; e.g. with uncertain Dirichlet boundary conditions. T2 - EMI 2024 IC – ASCE Engineering Mechanics Institute 2024 International Conference CY - Vienna, Austria DA - 11.09.2024 KW - Bayesian inference KW - Gradient damage model KW - Full-field measurements PY - 2024 AN - OPUS4-62248 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Saif-Ur-Rehman, Saif-Ur-Rehman T1 - Failure prediction during 3D concrete printing: a numerical simulation aproach N2 - 3D concrete printing is a technology that promises to bring digitalization in the construction phase of E&C industry. It brings automation in the construction process by removing the need for formwork and providing a much better geometrical flexibility in terms of construction of complex structures. However, owing to the novelty of the technology, it brings an added complexity compared to traditional methods which needs to be studied. As many parameters are involved in the process, a detailed knowledge about the parameters and their influence on the mechanical behaviour of the object being printed is limited. Because of this, these parameters are commonly determined using a trial-and-error approach which can lead to unforeseen failure during the printing process. A systematic approach to determine the optimal set of parameters is required so that the mechanical behaviour of the (freshly) printed concrete can be controlled. This work is aimed towards achieving a better understanding of the various parameters involved in the 3DCP process and how they affect the mechanical behaviour of the object being printed using a modeling and simulation approach. Additionally, it seeks to develop optimization strategies and process control methods based on numerical models. A physics based numerical model is developed with the focus on capturing the correct material response using von-mises plasticity model with non-linear isotropic hardening. An objective stress rate is used to account for geometric non-linearity. Time dependent material parameters are used to replicate the aging effect of concrete. A density-based step by step layer activation approach is used for the structural model to mimic the layer-by-layer printing process. Both failure modes namely plastic collapse and elastic buckling are observed qualitatively. Model calibration and quantitative comparison with experimental results are planned for future work. Furthermore, development of optimization strategies and building a digital twin of the process using the developed model and sensor data from the printer are also planned. T2 - Additive Manufactured Concrete Structures 2024 CY - Naples, Italy DA - 15.07.2024 KW - 3D Concrete Printing KW - Material Modeling KW - Simulation PY - 2024 AN - OPUS4-62088 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 - CONF A1 - Tamsen, Erik T1 - From concrete mixture to structural design an optimization framework to reduce the global warming potential N2 - Concrete has a long history in the construction industry and is currently one of the most widely used building materials. Unfortunately, the concrete industry has a significant impact on the environment by contributing to about 9% of the total anthropogenic greenhouse gas (GHG) emissions. Concrete is a highly complex composite material. However, the main source of concrete's GHG emissions is the cement. This leads to two main strategies when trying to reduce the environmental impact. The first is to reduce the cement within the concrete mix. This can be done by substituting it using additives or increasing the amount of aggregates. Usually this will lead to decreased material properties, like compressive strength or stiffness. The second option is to reduce the amount of required concrete by optimizing the topology of the structure. However, this might require higher compressive strength. In addition, there are other properties like to workability which need to be considered. All in all, this leads to a highly complex optimization problem, which requires the estimation of effective concrete properties, based on the mixture as input to a predictive simulation. We present an automated workflow framework which combines experimental data with simulations, calibrates the simulation and performs the desired optimization. This workflow includes classical FE models, design guidelines based on model codes, as well as data driven methods. The chosen example is a beam, for which the concrete mixture is optimized to reduce GHG emissions. The first step is an estimation of material parameters, based on experimental data. This includes measures of stochastic distribution, allowing the quantification of the quality of the estimated parameters. The second step is the optimization. It takes into account constraints like the loading capacity after 28 days, the maximum allowed temperature during cement hydration and the maximum time till demoulding. The applied models include a Mori-Tanaka-based homogenization method to estimate effective concrete parameters, an FE simulation including the evolution of the concrete compressive strength and stiffness, the temperature field, displacements, and stress. This research shows a way towards a more performance-oriented material design. T2 - M2P 2023 (First International Conference Math 2 Product) CY - Taormina, Italy DA - 30.05.2023 KW - Experimental data KW - Simulations PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-582545 AN - OPUS4-58254 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 - Andrés Arcones, Daniel T1 - Uncertainty Quantification and Model Extension for Digital Twins of Bridges through Model Bias Identification N2 - The creation and use of Digital Twins of existing structures, such as bridges, implies precise digital replicas that accurately mirror their physical counterparts. Ensuring the trustworthiness of Digital Twins and facilitating informed decision-making necessitates a robust approach to Uncertainty Quantification (UQ). A suitable model-updating scheme is key in preserving the quality and robustness of simulation-based Digital Twins. Model bias, stemming from discrepancies between computational models and real-world systems, poses a significant challenge in achieving this goal. This study delves into the challenges posed by model bias within Bayesian updating of Digital Twins of bridges. Two alternative model bias identification methods —a modularized version of Kennedy and O’Hagan’s approach and another one based on Orthogonal Gaussian Processes — are evaluated in comparison with the classical Bayesian inference framework. A key innovation lies in the modification of the aforementioned approaches to incorporate additional information into the Digital Twin framework via the bias term. This enables the extension of the model non-intrusively, leveraging large pools of data inherent in Digital Twins. The study showcases the potential of this approach to correct predictions, quantify uncertainties, and enhance the system with previously untapped information. This underscores the importance of everaging available data within Digital Twins to identify deficiencies and guide potential future model improvements. T2 - ECCOMAS CONGRESS 2024 9th European Congress on Computational Methods in Applied Sciences and Engineering CY - Lisbon, Portugal DA - 03.06.2024 KW - Digital Twin KW - Simulation Models KW - Uncertainty Quantification KW - Model Bias PY - 2024 AN - OPUS4-61023 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Andrés Arcones, Daniel A1 - Weiser, M. A1 - Koutsourelakis, F. A1 - Unger, Jörg F. T1 - A Bayesian Framework for Simulation-based Digital Twins of Bridges N2 - Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their tructural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management. T2 - Eurostruct 2023 CY - Vienna, Austria DA - 25.09.2023 KW - Digital Twins KW - Bayesian Inference KW - Bridge Monitoring KW - Uncertainty Quantification PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-586803 UR - https://eurostruct.org/eurostruct-2023/ DO - https://doi.org/10.1002/cepa.2177 SN - 2509-7075 VL - 6 IS - 5 SP - 734 EP - 740 PB - Ernst & Sohn CY - Berlin AN - OPUS4-58680 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - 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 -