TY - JOUR A1 - Herrmann, Ralf A1 - Ramasetti, Eshwar Kumar A1 - Degener, Sebastian A1 - Hille, Falk A1 - Baeßler, Matthias T1 - A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics N2 - The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam DA - 10.06.2024 KW - Nibelungen Bridge KW - Living Lab KW - Transfer Learning KW - Transfer Structures KW - Modal Analysis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612810 UR - https://www.ndt.net/search/docs.php3?id=29853 DO - https://doi.org/10.58286/29853 SN - 1435-4934 VL - 29 IS - 7 SP - 1 EP - 8 PB - NDT.net GmbH & Co. KG CY - Mayen AN - OPUS4-61281 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Herrmann, Ralf A1 - Ramasetti, Eshwar Kumar A1 - Ponnam, Poojitha A1 - Degener, Sebastian ED - Briffaut, Matthieu ED - Torrenti, Jean Michel T1 - Characterization of Smart Acceleration Sensors for Traffic Recognition using AI at the Nibelungen Bridge Worms N2 - The integration of digital sensors into Structural Health Monitoring (SHM) systems presents both significant opportunities and challenges, particularly in terms of sensor data management, bandwidth optimization, and system performance enhancement. This study examines the use of smart digital acceleration sensors, specifically MEMS accelerometers with CAN bus interfaces, deployed on the Nibelungen Bridge in Worms, Germany. The research evaluates the sensors' performance and calibration through laboratory and in-situ measurements, focusing on traffic load detection for vehicle recognition using Artificial Intelligence (AI) techniques. Additionally, the potential of AI, particularly autoencoders, in mitigating measurement uncertainties for traffic load detection is explored. T2 - 2025 fib International Symposium CY - Antibes, France DA - 16.06.2025 KW - SHM KW - Transfer Learning KW - SPP100+ KW - Nibelungen Bridge KW - Calibration KW - MEMS PY - 2025 UR - https://shop.fib-international.org/publications/fib-proceedings/1046-21th-fib-Symposium-Proceedings-in-Antibes-2025-France SN - 978-2-940643-29-5 SN - 2617-4820 SP - 3207 EP - 3816 CY - Antibes AN - OPUS4-64848 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -