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 - Degener, Sebastian T1 - Load Identification and Damage Detection by a Digital Twin of a Bridge applying Bayesian Model Calibration N2 - A digital twin for decision making during the life cycle of civil infrastructure is a promising concept combining simulation models with corresponding structure-specific sensor data to support maintenance decisions or to investigate the reliability. The sensor data quality as well as the model quality of the digital twin, comprising on modeling assumptions and correct model parameters strongly influences the prognosis results. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam, Germany DA - 10.06.2024 KW - Load identification KW - Damage detection KW - Digital Twin KW - Bayesian Model Calibration KW - Bridge model PY - 2024 AN - OPUS4-62186 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -