TY - JOUR A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Hille, Falk A1 - Herrmann, Ralf A1 - Baeßler, Matthias T1 - Vibration-based system identification of a large steel box girder bridge N2 - The Bundesanstalt für Materialforschung und -prüfung (BAM) collaborates with TNO to develop a software framework for automated calibration of structural models based on monitoring data. The ultimate goal is to include such models in the asset management process of engineering structures. As a basis for developing the framework, a multi-span road bridge consisting of ten simply supported steel box girders was selected as a test case. Our group measured output-only vibration data from one box girder under ambient conditions. From the data, we determined eigenfrequencies and mode shapes. In parallel, we developed a preliminary structural model of the box girder for the purpose of predicting its modal properties. In this contribution, we provide an overview of the measurement campaign, the operational modal analysis, the structural modeling and qualitatively compare the identified with the predicted modes. As an outlook, we discuss the further steps in the calibration process and future applications of the calibrated model. T2 - XII International Conference on Structural Dynamics (EURODYN 2023) CY - Delft, The Netherlands DA - 02.07.2023 KW - Verkehrsinfrastukturen KW - SHM KW - Model updating KW - System identification KW - Operational modal analysis PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-605041 DO - https://doi.org/10.1088/1742-6596/2647/18/182039 SN - 1742-6596 VL - 2647 IS - 18 SP - 1 EP - 9 PB - IOP Publishing CY - Bristol AN - OPUS4-60504 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias A1 - Morgenthal, Guido ED - Cha, Young-Jin T1 - A Bayesian Probabilistic Framework for Building Models for Structural Health Monitoring of Structures Subject to Environmental Variability N2 - Managing aging engineering structures requires damage identification, capacity reassessment, and prediction of remaining service life. Data from structural health monitoring (SHM) systems can be utilized to detect and characterize potential damage. However, environmental and operational variations impair the identification of damages from SHM data. Motivated by this, we introduce a Bayesian probabilistic framework for building models and identifying damage in monitored structures subject to environmental variability. The novelty of our work lies (a) in explicitly considering the effect of environmental influences and potential structural damages in the modeling to enable more accurate damage identification and (b) in proposing a methodological workflow for model‐based structural health monitoring that leverages model class selection for model building and damage identification. The framework is applied to a progressively damaged reinforced concrete beam subject to temperature variations in a climate chamber. Based on deflections and inclinations measured during diagnostic load tests of the undamaged structure, the most appropriate modeling approach for describing the temperature‐dependent behavior of the undamaged beam is identified. In the damaged state, damage is characterized based on the identified model parameters. The location and extent of the identified damage are consistent with the cracks observed in the laboratory. A numerical study with synthetic data is used to validate the parameter identification. The known true parameters lie within the 90% highest density intervals of the posterior distributions of the model parameters, suggesting that this approach is reliable for parameter identification. Our results indicate that the proposed framework can answer the question of damage identification under environmental variations. These findings show a way forward in integrating SHM data into the management of infrastructures. KW - Verkehrsinfrastukturen KW - SHM KW - Model Updating KW - Environmental and Operational Variability KW - Damage Identification KW - Model Building PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-605027 DO - https://doi.org/10.1155/2024/4204316 SN - 1545-2255 VL - 2024 IS - 1 SP - 1 EP - 23 PB - Wiley AN - OPUS4-60502 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Herrmann, Ralf 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 - TY - CONF A1 - Baeßler, Matthias T1 - Anwendungsszenarien und Referenzanwendungen sensorbasiertes Monitoring N2 - Präsentiert wird eine Kategorisierung von Anwendungsszenarien für sensorbasiertes Monitoring und die Planung für die Referenzbauwerke mit SHM-Anwendungen. Im Rahmen des Forschungsvorhabens Aistec Pro werden Anwendungsszenarien für sensorbasiertes Monitoring kategorisiert und in Referenzanwendungen umgesetzt. Der Vortrag gibt einen Überblick über die abgeschlossene Planung. T2 - Jahrestreffen Verbund Aistec-Pro CY - Bonn, Germany DA - 17.09.2025 KW - SHM KW - Monitoring KW - Brückensicherheit KW - Aistec Pro PY - 2025 AN - OPUS4-64124 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liao, Chun-Man A1 - Niederleithinger, Ernst A1 - Bernauer, F A1 - Igel, H A1 - Hadziioannou, Céline T1 - Wave-Screening Methods for Prestress-Loss Assessment of a Large-Scale Post-Tensioned Concrete Bridge Model Under Outdoor Conditions N2 - This paper presents advancements in structural health monitoring (SHM) techniques, with a particular focus on wave-screening methods for assessing prestress loss in a large-scale prestressed concrete (PC) bridge model under outdoor conditions. The wave-screening process utilizes low-frequency wave propagation obtained from seismic interferometry of structural free vibrations and high-frequency wave propagation obtained through ultrasonic transducers embedded in the structure. An adjustable post-tensioning system was employed in a series of experiments to simulate prestress loss. By comparing bridge vibrations under varying post-tensioning forces, the study investigated prestress loss and examined temperature-related effects using the coda wave interferometry (CWI) method. Local structural alterations were analyzed through wave velocity variations, demonstrating sensitivity to bridge temperature changes. The findings indicate that wave-based methods are more effective than traditional modal analysis for damage detection, highlighting the dual impacts of prestress loss and temperature, as well as damage localization. This study underscores the need for long-term measurements to account for temperature fluctuations when analyzing vibration measurements to investigate changes in prestressing force in PC structures. KW - Coda wave interferometry KW - Damage detection KW - Prestress loss KW - Seismic interferometry KW - SHM KW - Temperature influence KW - Ultrasonics KW - Wave-screening PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-634244 UR - https://www.mdpi.com/2076-3417/15/11/6005 DO - https://doi.org/10.3390/app15116005 VL - 15 IS - 11 SP - 1 EP - 18 PB - MDPI AN - OPUS4-63424 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Gerards-Wünsche, Paul A1 - Hille, Falk T1 - Zuverlässigkeitsbasierter Technologietransfer am Beispiel Risslumineszenz Von der ZfP im Labor zum KI-basierten SHM-System für die Industrie – Statusbericht N2 - Der Übergang neuer zerstörungsfreien Prüfmethoden (ZfP) aus dem Labor in die industrielle Anwendung stellt erhebliche Herausforderungen dar. Für visuelle ZfP-Techniken, wie die in dieser Studie verwendete Risslumineszenz-Methode, ist die Automatisierung der Fehlererkennung durch KI-gestützte Computer-Vision-Systeme ein logischer nächster Schritt. Findet dies als kontinuierliche Überwachung statt, entwickeln sich solche Systeme zu einer KI-gestützten SHM-Methode. Die Implementierung dieser Technologien in sicherheitskritischen Anwendungen erfordert jedoch einen robusten Nachweis ihrer Zuverlässigkeit, der durch diese Weiterentwicklung an Komplexität gewinnt. Während etablierte Standards für Probability of Detection (POD)-Studien in ZfP-Systemen existieren, fehlen entsprechende Normen für SHM-Systeme, insbesondere solche, die KI einsetzen. Obwohl statistische Methoden für SHM-Systeme verfügbar sind, um Datenabhängigkeiten zu berücksichtigen, ist ein ganzheitlicher POD-Ansatz notwendig, um beeinflussende und variable Störfaktoren umfassend zu adressieren und die Alterung des SHM-Systems zu berücksichtigen. Diese Arbeit demonstriert Fortschritte bei der Entwicklung einer maßgeschneiderten Methodik, die auf die spezifischen Anforderungen zugeschnitten ist, um die Zuverlässigkeit eines KI-gestützten SHM-Systems auf Basis der Risslumineszenz nachzuweisen. Dieser Ansatz integriert die Prinzipien von POD-Studien mit statistischen Methoden für SHM-Systeme, um eine robuste Zuverlässigkeitsbewertung zu gewährleisten. Er verdeutlicht die erheblichen Herausforderungen, die bereits bei der kontinuierlichen Überwachung einer relativ einfachen visuellen Methode wie der Risslumineszenz auftreten. T2 - DGZfP-Jahrestagung 2025 CY - Berlin, Germany DA - 25.05.2025 KW - Risslumineszenz KW - SHM KW - PoD KW - Zuverlässigkeit KW - ZfP PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-636142 SP - 1 EP - 13 PB - NDT.net AN - OPUS4-63614 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias A1 - Ebell, Gino A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Schneider, Ronald ED - Lienhart, Werner ED - Krüger, Markus T1 - On potentials and challenges of physics-informed SHM for civil engineering structures N2 - Physics-informed structural health monitoring, which integrates realistic physical models of material behavior, structural response, damage mechanisms, and aging processes, offers a promising approach to improve monitoring capabilities and inform operation and maintenance planning. However, the associated technical challenges and model requirements are context-specific and vary widely across applications. To illustrate the relevance and potential of the topic, two application examples are presented. The first focuses on monitoring the modal characteristics of a prestressed road bridge, where strong sensitivity to temperature variations limits the diagnostic capabilities of conventional vibration-based global monitoring. The discussion highlights how environmental influences can obscure structural changes, and emphasizes that purely data-based approaches are inherently limited to detecting anomalies and do not enable comprehensive condition diagnostics. The second example explores a physics-informed monitoring approach for prestressed concrete bridges affected by hydrogen-induced stress corrosion cracking. T2 - SHMII-13 CY - Graz, Austria DA - 01.09.2025 KW - Hydrogen Stress Corrosion Cracking KW - SHM KW - Physics informed PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-643271 SN - 978-3-99161-057-1 DO - https://doi.org/10.3217/978-3-99161-057-1-039 SP - 245 EP - 251 PB - Verlag der Technischen Universität Graz CY - Graz, Austria AN - OPUS4-64327 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Baeßler, Matthias T1 - On potentials and challenges of physics-informed SHM for civil engineering structures N2 - Physics-informed structural health monitoring, which incorporates realistic physical models of material behavior, structural response, damage mechanisms, and aging processes, offers a promising framework to enhance monitoring capabilities and inform operation and maintenance planning. Nevertheless, the technical challenges and model requirements associated with this approach are highly context-dependent and can vary significantly across different applications. The presentation focusses on two case studies that highlight challenges and progress in Physics informed SHM. T2 - SHMII-13 CY - Graz, Austria DA - 01.09.2025 KW - SHM KW - Physics informed KW - Hydrogen Stress Corrosion Cracking PY - 2025 AN - OPUS4-64326 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 - TY - CONF A1 - Kang, Chongjie A1 - Herrmann, Ralf A1 - Eisermann, Cedric A1 - Marx, Steffen ED - Chang, Fu-Kuo ED - Guemes, Alfredo T1 - Collaborative Structural Health Monitoring for Bridge Digital Twins N2 - Structural Health Monitoring (SHM) is an effective tool that not only reduces reliance on periodic inspections but also enhances them by analyzing the current state of a structure based on the latest structural data. Collaborative SHM, which integrates various SHM systems within the scope of bridge digital twins (BDTs), enhances infrastructure resilience and maintenance strategies. However, it faces challenges in integrating distributed sensor networks and requires interdisciplinary collaboration. In this work, various aspects of enhancing collaborative SHM with BDTs are presented. As a pilot project, the Nibelungen Bridge in Worms (NBW), Germany, is introduced. Based on specific stakeholder and project requirements, various SHM systems havebeen installed on this bridge. To address these challenges, goal-oriented solutions have been developed and elaborated. Finally, conclusions and future outlooks are presented. T2 - 15th International Workshop on Structural Health Monitoring CY - Stanford, CA, USA DA - 09.09.2025 KW - SHM KW - Collaborative Sensing KW - Nibelungen Bridge KW - SPP100+ PY - 2025 SN - 978-1-60595-699-2 DO - https://doi.org/10.12783/shm2025/37546 SP - 2305 EP - 2312 PB - DEStech Publications CY - Lancaster, PA, USA AN - OPUS4-64851 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -