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 JF - Engineering reports 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 - 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 JF - Proceedings in Applied Mathematics & Mechanics 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 - CONF A1 - Jafari, Abbas A1 - Titscher, Thomas A1 - Chatzi, E. A1 - Unger, Jörg F. T1 - Variational Bayesian inference of damage in concrete material using spatially-dense data N2 - Numerical simulators, such as finite element models, have become increasingly capable of predicting the behaviour of structures and components owing to more sophisticated underlying mathematical models and advanced computing power. A common challenge lies, however, in calibrating these models in terms of their unknown/uncertain parameters. When measurements exist, this can be achieved by comparing the model response against measured data. Besides uncertain model parameters, phenomena like damage can give rise to further uncertainties; in particular, quasi-brittle materials, like concrete, experience damage in a heterogeneous manner due to various imperfections, e.g. in geometry and boundary conditions. This hardens an accurate prediction of the damaged behaviour of real structures that comprise such materials. In this study, which draws from a data-driven approach, we use the force-version of the finite element model updating method (FEMU-F) to incorporate measured displacements into the identification of the damage parameters, in order to cope with heterogeneity. In this method, instead of conducting a forward evaluation of the model and comparing the model response (displacements) against the data, we impose displacements to the model and compare the resulting force residuals with measured reaction forces. To account for uncertainties in the measurement of displacements, we endow this approach with a penalty term, which reflects the discrepancy between measured and imposed displacements, where the latter is assumed as unknown random variables to be identified as well. A Variational Bayesian approach is used as an approximating tool for computing posterior parameters. The underlying damage model considered in this work is a gradient-enhanced damage model. We first establish the identification procedure through two virtual examples, where synthetic data (displacements) are generated over a certain spatially-dense set of points over the domain. The procedure is then validated on an experimental case-study; namely a 3-point bending experiment with displacement measurements resulting from a digital image correlation (DIC) analysis. T2 - MSE 2022 CY - Online meeting DA - 27.09.2022 KW - Heterogeneity KW - Gradient damage KW - Model updating KW - Variational Bayesian KW - Concrete PY - 2022 AN - OPUS4-56625 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -