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 - CONF A1 - Herrmann, Ralf A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias T1 - SHM system integration and experiments at a high speed railway bridge N2 - The long-term preservation of our infrastructure requires not only intelligent sensor technology and highly developed monitoring procedures, but also innovative digital tools for analyzing, evaluating and utilizing the results. This includes mathematical and, in particular, probabilistic methods for damage detection and tracking as well as for calculating service life and maintenance cycles and data management. The example project Maintal Bridge Gemuenden as part of the AISTEC project shows the workflow for the implementation of structural health monitoring and experimental tests with a train of Deutsche Bahn. The influence lines, as one possible way for damage detection, were measured with a highly accurate GNSS System to locate the trains position when crossing the bridge. The results were compared to measurements from 1987 just before the bridge went in operation. T2 - Structural Health Monitoring Using Statistical Pattern Recognition CY - Berlin, Germany DA - 20.03.2023 KW - SHM KW - Maintal Bridge Gemuenden KW - Load Test KW - Damage Detection KW - Railway PY - 2023 AN - OPUS4-57242 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -