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 - 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. A1 - Robens-Radermacher, Annika A1 - Held, Felix A1 - Coelho Lima, Isabela T1 - Efficient reliability analysis with model reduction techniques N2 - One of the most important goals in civil engineering is to guaranty the safety of constructions. National standards prescribe a required failure probability in the order of 10-6 (e.g. DIN EN 199:2010-12). The estimation of these failure probabilities is the key point of structural reliability analysis. Generally, it is not possible to compute the failure probability analytically. Therefore, simulation-based methods as well as methods based on surrogate modelling or response surface methods have been developed. Nevertheless, these methods still require a few thousand evaluations of the structure, usually with finite element (FE) simulations, making reliability analysis computationally expensive for relevant applications. The aim of this contribution is to increase the efficiency of structural reliability analysis by using the advantages of model reduction techniques. Model reduction is a popular concept to decrease the computational effort of complex numerical simulations while maintaining a reasonable accuracy. Coupling a reduced model with an efficient variance reducing sampling algorithm significantly reduces the computational cost of the reliability analysis without a relevant loss of accuracy. T2 - MathMet2019 CY - Lisbon, Portugal DA - 20.11.2019 KW - Reliability method KW - Numerical example PY - 2019 AN - OPUS4-49989 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -