TY - CONF A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Efficient reliability analysis coupling important sampling using adaptive subset simulation and PGD model reduction N2 - The key point of structural reliability analysis is the estimation of the failure probability (Pf), typically a rare event. This probability is defined as the integral over the failure domain which is given by a limit state function. Usually, this function is only implicit given by an underlying finite element simulation of the structure. It is generally not possible to solve the integral for Pf analytically. For that reason, simulation-based methods as well as methods based on surrogate modeling (or Response surface methods) has been developed. Nevertheless, these variance reducing methods still require a few thousand calculations of the underlying finite element model, making reliability Analysis computationally expensive for real applications. T2 - GAMM - Gesellschaft für Angewandte Mathematik und Mechanik e.V. CY - Vienna, Austria DA - 18.02.2019 KW - PGD model reduction PY - 2019 AN - OPUS4-48628 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Held, Felix A1 - Unger, Jörg F. T1 - Efficient reliability analysis by combining uncertain measurement data, Bayesian model updating and reduced order modeling N2 - The efficiency of structural model updating and the subsequent reliability analysis is increased by using the advantages of reduced order models. Coupling a reduced model of the structure of interest with a Bayesian model updating approach or an reliability analysis to estimate the failure probability reduce the computational cost of such complex analyses drastically. T2 - 8th Workshop on High-Dimensional Approximation (HDA) 2019 CY - Zurich, Switzerland DA - 09.09.2019 KW - Reduced order modeling KW - Bayesian model updating PY - 2019 AN - OPUS4-49035 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Coupling PGD model reduction with importance sampling using adaptive subset simulation for reliability analysis N2 - The key point of structural reliability analysis is the estimation of the failure probability. This probability is defined as the integral over the failure domain which is given by a limit state function. Usually, this function is only implicit given by an underlying finite element simulation of the structure. It is generally not possible to solve the integral analytically. For that reason, numerical methods based on sampling and surrogates have been developed. Nevertheless, these sampling methods still require a few thousand calculations of the underlying finite element model, making reliability analysis computationally expensive for relevant applications. Coupling a reduced order model (proper generalized decomposition) with an efficient variance reducing sampling algorithm can reduce the computational cost of reliability analysis drastically. In the proposed method, an importance sampling technique is coupled with a reduced structural model by means of PGD to estimate the failure probability. Instead of calculating the design point e.g. with optimization algorithms, the design point is adaptively estimated by using the idea of subset simulation. The failure probability is estimated in an iterative scheme based on adaptively computing the design point and refining the PGD model. T2 - 5th international Workshop Reduced Basis, POD and PGD Model Reduction Techniques (MORTech) 2019 CY - Paris, France DA - 19.11.2019 KW - Reliability KW - Probability of failure KW - Importance sampling KW - Proper Generalized Decomposition KW - Reduced order models PY - 2019 AN - OPUS4-49801 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Coehlo Lima, Isabela A1 - Unger, Jörg F. T1 - Model identification coupling Bayesian Inference with PGD reduced models with random field material parameters N2 - The main challenge using numerical models as digital twins in real applications is the calibration and validation of the model based on uncertain measurement data. Therefore, model updating approaches which are inverse optimization processes are applied. This requires a huge number of computations of the same numerical model with slightly different model parameters. For that reason, model updating becomes computationally very expensive for real applications. Model reduction, e.g. the proper generalized decomposition method, is a popular concept to decrease the computational effort of complex numerical simulations. Therefore, a reduced model of the structure of interest is derived and will be used as surrogate model in a Variational Bayesian procedure to create a very efficient digital twin of the structure. An efficient model updating approach by means of a PGD reduced model with random field material stiffness parameters is shown. The random field allows, to calibrate the model considering parameter changes over the spatial direction. These changes can be caused by local damages as well as by production. As an exemplary application a demonstrator bridge is used. Digital twins can reduce the costs for maintenance and inspections especially for the costly civil infrastructure with high requirements at their performance over the whole lifetime. Currently, the current state of the structure is determined by regular manual and visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. In this case, model-based approaches where a digital twin is set up can improve the process. Based on this digital twin, a prognosis of the future performance of the structure, e.g. the failure probability, can be computed. The influences of the reduction degree, the mesh discretization as well as the correlation length in the PGD Bayesian approach are studied by means of the digital twin of a simple pre-stressed concrete two field bridge. T2 - Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM) 2020@21 CY - Online meeting DA - 15.03.2021 KW - Digital twin supporting KW - Proper generalized decomposition PY - 2021 AN - OPUS4-52317 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Kujath, Cezary A1 - Bos, F. A1 - Mechtcherine, V. A1 - Unger, Jörg F. T1 - Advantages and challenges of data stores for interlaboratory studies – an example from mechanical test data of printed concrete structures N2 - Interlaboratory studies are common tools for collecting comparable data to implement standards for new materials or testing technologies. In the case of construction materials, these studies form the basis for recommendations and design codes. Depending on the study, the amount of data collected can be enormous, making manual handling and evaluation difficult. On the other hand, the importance of the FAIR (findable, accessible, interoperable, and reusable) principles for scientific data management, published by Wilkinson et al. in 2016, is constantly growing and changing the view on data usage. The benefits of using data management tools such as data stores/repositories or electronic laboratory notebooks are many. Data is stored in a structured and accessible way (at least within a group) and data loss due to staff turnover is reduced. Tools usually support data publishing and analysis interfaces. In this way, data can be reused years later to generate new knowledge with future insights. On the other hand, there are many challenges in setting up a data repository, such as selecting suitable software tools, defining the data structure, enabling data access, and understanding by others and ensuring maintenance, among others. This talk discusses the advantages and challenges of setting up and applying a data repository using the interlaboratory study on the mechanical properties of printed concrete structures carried out in RILEM TC 304-ADC as example. First, the definition of a suitable data structure including all information is discussed. The tool-dependent upload process is then described. Here, the data management system openBIS (open source software developed by ETH Zurich) is used. Since in most cases an open compute platform allowing access from different organisations is not possible or available due to data protection and maintenance issues, tool-independent export options are discussed and compared. Finally, the different query and analysis possibilities are demonstrated. T2 - RILEM spring convention & conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milan, Italy DA - 07.04.2024 KW - Data stores KW - Data structuring KW - Data evaluation KW - Automatization KW - Interlaboratory PY - 2024 UR - https://www.rilem.net/agenda/rilem-spring-convention-conference-on-advanced-construction-materials-and-processes-for-a-carbon-neutral-society-1530 AN - OPUS4-59906 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Mezhov, Alexander A1 - Unger, Jörg F. A1 - Schmidt, Wolfram T1 - Temperature dependent modelling approach for early age behavior of printable mortars N2 - For extrusion-based 3D concrete printing, the early age mechanical behavior is influenced by various time dependent phenomena: structural build-up, plasticity as well as viscosity. The structural build-up is governing the stability and early-age strength development of the fresh printable cementitious materials and with that influencing the printability, buildability, and open time of the printing process. Generally, it is influenced by a number of factors, i.e. composition of the printable material, printing regime, and ambient conditions (temperature, humidity, etc.). There are several approaches to model the structural build-up of cementitious materials. All models are based on a time-dependent internal structural parameter describing the flocculation state, which is assumed to be zero after mixing and increases with time. The approaches differ in the definition of the time dependency (linear, exponential, bi-linear). Usually, the parameters are defined for a specific material composition without considering the influence of ambient conditions. In this contribution, the bi-linear structural build-up model [Kruger et al., Construction and Building Materials 224, 2019] is extended by the temperature influence. Temperature changes will occur in real life printing processes due to changing ambient conditions (summer, winter, day, night) as well as the printing process (pressure changes etc.) and have a significant impact on the structural build-up process: an increase of the temperature leads to a faster dissolution of cement phases, accelerates hydration and boosts the Brownian motion. For that reason, the model parameters are simulated as temperature dependent using an Arrhenius function. Furthermore, the proposed extended model is calibrated based on measurement data using Bayesian inference. A very good agreement of the predicted model data with the measured control data was reached. Additionally, the structural build-up model is integrated into a viscoelastic and elastoplastic mechanical model, simulating the whole mechanical behavior during layer deposition. T2 - Eighth International Symposium on Life-Cycle Civil Engineering (IALCCE 2023) CY - Milan, Italy DA - 03.07.2023 KW - 3D concrete printing KW - Material characterization KW - Structural build-up KW - Thixotropy KW - Model calibration PY - 2023 AN - OPUS4-58218 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Coelho Lima, Isabela A1 - Unger, Jörg F. T1 - Efficient model identification using a PGD forward model - Influence of surrogate accuracy and converergence approach N2 - There is a rising attention of using numerical models for effcient structural monitoring and ensuring the structure's safety. Setting up virtual models as twin for real structures requires a model identification process calculating the unknown model parameters, which mostly are only indirectly measurable. This is a computationally very costly inverse optimization process, which often makes it unfeasible for real applications. Effcient surrogate models such as reduced order models can be used, to overcome this limitation. But the influence of the model accuracy on the identification process has then to be considered. The aim is to automatically control the influence of the model's accuracy on the identification. Here, a variational Bayesian inference approach[3] is coupled with a reduced forward model using the Proper Generalized Decomposition (PGD) method. The influence of the model accuracy on the inference result is studied and measured. Therefore, besides the commonly used Bayes factor the Kullback-Leibler divergences between the predicted posterior pdfs are proposed. In an adaptive inference procedure, the surrogate's accuracy is iteratively increased, and the convergence of the posterior pdf is analysed. The proposed adaptive identification process is applied to the identification of spatially distributed damage modeled by a random eld for a simple example with synthetic data as well as a small, reinforced bridge with real measurement data. It is shown that the proposed criteria can mirror the influence of the model accuracy and can be used to automatically select a suffciently accurate surrogate model. T2 - The 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) 2022 CY - Oslo, Norway DA - 05.06.2022 KW - Model order reduction KW - Model identification KW - Bayes factor KW - PGD KW - Kullback-Leibner divergence PY - 2022 AN - OPUS4-55112 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mezhov, Alexander A1 - Robens-Radermacher, Annika A1 - Zhang, Kun A1 - Kühne, Hans-Carsten A1 - Unger, Jörg F. A1 - Schmidt, Wolfram T1 - Temperature Impact on the Structural Build-Up of Cementitious Materials - Experimental and Modelling Study N2 - With increasing focus on industrialized processing, investigating, understanding, and modelling the structural build-up of cementitious materials becomes more important. The structural build-up governs the key property of fresh printable materials -- buildability -- and it influences the mechanical properties after the deposition. The structural build-up rate can be adjusted by optimization of the mixture composition and the use of concrete admixtures. Additionally, it is known, that the environmental conditions, i.e. humidity and temperature have a significant impact on the kinetic of cement hydration and the resulting hardened properties, such as shrinkage, cracking resistance etc. In this study, small amplitude oscillatory shear (SAOS) tests are applied to examine the structural build-up rate of cement paste subject to different temperatures under controlled humidity. The results indicate significant influences of the ambient temperature on the intensity of the re-flocculation (Rthix) rate, while the structuration rate (Athix) is almost not affected. A bi-linear thixotropy model extended by temperature dependent parameters coupled with a linear viscoelastic material model is proposed to simulate the mechanical behaviour considering the structural build-up during the SAOS test. T2 - Third RILEM International Conference on Concrete and Digital Fabrication (Digital Concrete 2022) CY - Loughborough, UK DA - 27.06.2022 KW - Structural build-up KW - Rheological properties KW - Modelling PY - 2022 AN - OPUS4-55581 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Coehlo Lima, Isabela A1 - Unger, Jörg F. T1 - Stiffness identification by efficient model calibration of random field variables for the Young's modulus N2 - The main challenge in using numerical models as digital twins in real applications for prognosis purposes, such as reliability analysis, is the calibration and validation of the models based on uncertain measurement data. Uncertainties are not limited to the measurement data, but the numerical model itself will not be perfect due to the modelling assumptions. In this contribution, a probabilistic inference method for model calibration, based on the Bayes’ Theorem, is used to face that issue. Such inference approaches include uncertainties on the data as well as on the model parameters, allowing to compute an a posteriori distribution for the model parameters as well as a noise term reflecting the measured data. However, such probabilistic inference methods require a lot of evaluations of the numerical forward model for different model parameters. An improvement of the efficiency is obtained by replacing the forward model with a reduced model. Model reduction, e.g. the proper generalized decomposition (PGD) method, is a popular concept to decrease the computational effort, where each evaluation of the reduced forward model is a pure less costly function evaluation. The heterogeneous spatial distribution of material parameters in the forward model is described by a lognormal random field. This allows identifying a variable stiffness over the spatial directions by identifying the random field variables with given measurement data. These changes can e.g. be caused by damage. The lognormal field is approximated as series expansion for the PGD problem. The derived efficient model identification procedure is shown using a real reinforced prestress demonstrator bridge and stereophotogrammetry measurement data. A digital twin for that demonstrator bridge is build up using a set of measurement data and verified by testing additional measurement data. PGD model error against the FEM model is discussed based on an importance sampling analysis computing the Bayes Factor. T2 - COMPLAS 2021 CY - Berlin, Germany DA - 08.09.2021 KW - Digital twin KW - Structure monitoring KW - Model updating KW - Proper generalized decomposition KW - Bayesian inferences PY - 2021 AN - OPUS4-53285 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -