TY - JOUR A1 - Bartels, Jan-Hauke A1 - Gündogdu, Berk A1 - Herrmann, Ralf A1 - Marx, Steffen T1 - Beschleunigungssensoren zur Zustandsüberwachung von Ingenieurbauwerken unter Einfluss von Umweltfaktoren bei tiefen Frequenzen T1 - Acceleration sensors for structural health monitoring of engineering structures under the influence of environmental factors at low frequencies N2 - Structural Health Monitoring (SHM) wird zunehmend zur kontinuierlichen Zustandsbewertung von Ingenieurbauwerken eingesetzt. Wichtige Bewertungsparameter sind globale Systemeigenschaften, wie z. B. Eigenfrequenzen, zu deren Bestimmung Beschleunigungssensoren eingesetzt werden. Häufig werden sog. MEMS-Sensoren (Micro Electro Mechanical Systems) verwendet, die jedoch ein hohes Rauschniveau aufweisen. Alternativ können rauschärmere IEPE-Sensoren (Integrated Electronics Piezo Electric) eingesetzt werden, die auch bei geringster Strukturanregung Schwingungen zuverlässig erfassen. Ferner besteht das Problem, dass Änderungen der Eigenfrequenzen infolge Bauwerksschädigung schwer von Änderungen der Eigenfrequenzen infolge Umwelteinflüssen zu unterscheiden sind. Letztere verändern die Eigenschaften der Struktur und die des Messsystems. Um Umwelteinflüsse auf das Messsystem im Anwendungsgebiet Ingenieurbau zu untersuchen, wurden IEPE-Beschleunigungsaufnehmer hinsichtlich ihres Übertragungsverhaltens im niederfrequenten Beschleunigungsbereich analysiert. Es zeigt sich, dass das Verhalten nicht nur frequenz-, sondern auch temperaturabhängig ist, während die Luftfeuchte keinen Einfluss hat. Diese für das Bauwerk unbedenklichen Einflüsse müssen für eine robuste Zustandsüberwachung kompensiert werden. Für die Anwendung im Ingenieurbau werden IEPE-Sensoren empfohlen, da sie ein hohes Signal-zu-Rausch-Verhältnis aufweisen und niederfrequente Bauwerksschwingungen zuverlässig erfassen. N2 - Acceleration sensors for structural health monitoring of engineering structures under the influence of environmental factors at low frequencies. Structural health monitoring (SHM) is increasingly used to continuously assess the condition of engineering structures. Important assessment parameters are global system properties, such as eigenfrequency, which are measured by accelerometers. Micro-electro-mechanical systems (MEMS) sensors are often used, but have a high noise level. Alternatively, low-noise IEPE (integrated electronics piezo electric) sensors can be used, which reliably detect vibrations even with the slightest structural excitation. Another problem is that changes in eigenfrequency due to structural damage are difficult to distinguish from changes in eigenfrequency due to environmental effects. The latter change the properties of both the structure and the measurement system. In order to investigate environmental effects on the measurement system in the field of civil engineering, IEPE accelerometers have been analyzed for their transmission behavior in the low-frequency acceleration range. It was found that the behavior is not only frequency dependent, but also temperature dependent, while humidity has no influence. These nonstructural effects must be compensated for to ensure robust condition monitoring. IEPE sensors are recommended for civil engineering applications because of their high signal-to-noise ratio and ability to reliably detect low-frequency structural vibrations. KW - Beschleunigungssensoren KW - Kalibrierung KW - Structural Health Monitoring KW - Umwelteinflüsse KW - Übertragungsverhalten KW - acceleration sensors KW - calibration KW - environmental influences KW - transmission behavior PY - 2024 DO - https://doi.org/10.1002/bate.202300056 SN - 1437-0999 SN - 0932-8351 VL - 101 IS - 10 SP - 1 EP - 11 PB - Ernst & Sohn CY - Berlin AN - OPUS4-60772 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bartels, Jan-Hauke A1 - Xu, Ronghua A1 - Kang, Chongjie A1 - Herrmann, Ralf A1 - Marx, Steffen T1 - Experimental Investigation on the Transfer Behavior and Environmental Influences of Low-Noise Integrated Electronic Piezoelectric Acceleration Sensors N2 - Acceleration sensors are vital for assessing engineering structures by measuring properties like natural frequencies. In practice, engineering structures often have low natural frequencies and face harsh environmental conditions. Understanding sensor behavior on such structures is crucial for reliable masurements. The research focus is on understanding the behavior of acceleration sensors in harsh environmental conditions within the low-frequency acceleration range. The main question is how to distinguish sensor behavior from structural influences to minimize errors in assessing engineering structure conditions. To investigate this, the sensors are tested using a long-stroke calibration unit under varying temperature and humidity conditions. Additionally, a mini-monitoring system configured with four IEPE sensors is applied to a small-scale support structure within a climate chamber. For the evaluation, a signal-energy approach is employed to distinguish sensor behavior from structural behavior. The findings show that IEPE sensors display temperature-dependent nonlinear transmission behavior within the low-frequency acceleration range, with humidity having negligible impact. To ensure accurate engineering structure assessment, it is crucial to separate sensor behavior from structural influences using signal energy in the time domain. This study underscores the need to compensate for systematic effects, preventing the underestimation of vibration energy at low temperatures and overestimation at higher temperatures when using IEPE sensors for engineering structure monitoring. KW - Acceleration sensors KW - Environmental influence KW - IEPE KW - Structural Health Monitoring KW - Low-frequency shaker PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-594623 UR - https://www.mdpi.com/2673-8244/4/1/4/ DO - https://doi.org/10.3390/metrology4010004 SN - 2673-8244 VL - 4 IS - 1 SP - 46 EP - 65 PB - MDPI CY - Basel AN - OPUS4-59462 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eichner, Lukas A1 - Gerards-Wünsche, Paul A1 - Happel, Karina A1 - Weise, Sigurd A1 - Haake, Gerrit A1 - Sieber, Lars A1 - Flederer, Holger A1 - Schneider, Ronald A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Baeßler, Matthias A1 - Huhn, Holger A1 - Küchler, Andreas T1 - Digitales Datenmanagement für die Instandhaltung von Offshore-Windparks T1 - Digital data management for maintenance in offshore wind farms N2 - Im Forschungsprojekt DiMoWind-Inspect wurde untersucht, welche Grundvoraussetzungen geschaffen werden müssen, um digitale Methoden für die Instandhaltung von Offshore-Windenergieanlagen einsetzen zu können. Daten aus allen Lebensphasen der Anlagen werden derzeit nur selten effizient dafür genutzt, um die sinnvollsten Instandhaltungsmaßnahmen am richtigen Ort, zum richtigen Zeitpunkt und mit den geringsten Kosten durchzuführen. Eine im Vorhaben entwickelte einheitliche Strukturierung der bisher häufig unstrukturierten Bau- und Instandhaltungsdaten ermöglicht ihre übergreifende, anwenderspezifische Verfügbarkeit. Hierfür werden Prinzipien des Referenzkennzeichnungssystems RDS-PP adaptiert. Es wird aufgezeigt, wie neben Bauteilen auch zusätzliche Informationen wie Instandhaltungsmaßnahmen oder Mängel strukturiert werden können. Dem Ansatz des Building Information Modeling folgend werden die Bauwerkskomponenten direkt mit den Informationen aus Inspektionen und weiteren Instandhaltungsmaßnahmen verknüpft. So können Informationen aus dem Betrieb über den Zustand der Struktur verarbeitet und für die maßgebenden Berechnungen und Nachweise zur Verfügung gestellt werden. Als Anwendungsfall wird ein zweistufiges Konzept vorgestellt, das zur Bewertung der Ermüdungslebensdauer eines korrosionsgeschädigten Konstruktionsdetails der Gründungsstruktur einer Offshore-Windenergieanlage mit Informationen aus der Instandhaltung eingesetzt wird. N2 - The DiMoWind-Inspect research project explored the essential requirements for implementing digital methods in the maintenance of offshore wind turbines. Currently, data from all stages of the turbines' lifecycles are underutilized, leading to suboptimal maintenance actions being taken in terms of location, timing, and cost. A consistent structuring of previously often unstructured construction and maintenance data developed in the project enables their cross-disciplinary, user-specific availability. To accomplish this, the principles of the Reference Designation System for Power Plants RDS-PP are applied. In this way, additional information, such as maintenance measures or defects, can be structured alongside components. The components of the structures are directly linked with information from inspections and other maintenance activities, following the Building Information Modeling approach. This allows for processing operational information about the condition of the structure and providing it for relevant calculations and assessments. As a use case, a two-stage concept is presented, utilizing maintenance information to assess the fatigue life of a corrosion-damaged structural detail in the support structure of an offshore wind turbine. KW - Building Information Modeling KW - Datenmanagement KW - Instandhaltung KW - Offshore-Windenergie KW - Referenzkennzeichnungssystem PY - 2024 DO - https://doi.org/10.1002/bate.202400026 VL - 101 IS - 10 SP - 558 EP - 567 PB - Ernst & Sohn CY - Berlin AN - OPUS4-60765 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Herrmann, Ralf T1 - Digitalisierung sichert die Bauwerke von gestern und das Wissen von morgen N2 - Die moderne Gesellschaft in Deutschland und Europa profitiert von der hervorragenden baulichen Infrastruktur, die für uns viele Aspekte aus den Bereichen Mobilität, Energieversorgung, Transport, Umweltschutz, (Nah-)Erholung und Gefahrenabwehr zur Alltäglichkeit werden lassen und uns ein hohes Gefühl der technischen Sicherheit vermitteln. Viele der Annehmlichkeiten, wie beispielweise ein Hochgeschwindigkeitseisenbahnverkehrsnetz, zuverlässige Stromversorgung im europäischen Verbundsystem und ein immenser Personen-, Güter- und Warenverkehr auf der Straße, zu Wasser und in der Luft ist ohne eine leistungsfähige, resiliente und zuverlässige bauliche Infrastruktur undenkbar. KW - Bauwerksmonitoring KW - Bauwerksdiagnostik KW - Digitalisierung PY - 2022 DO - https://doi.org/10.1002/bate.202270303 SN - 1437-0999 SN - 0932-8351 VL - 99 IS - 3 SP - 161 EP - 162 PB - Ernst & Sohn GmbH CY - Berlin AN - OPUS4-54557 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Munsch, Sarah Mandy A1 - Telong, Melissa A1 - Unger, Jörg F. A1 - Andrés Arcones, Daniel A1 - Pirskawetz, Stephan T1 - 63. DAfStb-Forschungskolloquium in der BAM - Themenblock 4: Digitalisierung im Bauwesen N2 - Die Digitalisierung hat sich in vielen Bereichen des Bauwesens durchgesetzt. So sind Planung und Entwurf selbst kleinerer Bauvorhaben heute vollständig digitalisiert. Auch das Monitoring von Bestandsbauwerken ist ohne digitale Datenerfassung, -verarbeitung und -speicherung nicht denkbar. Trotzdem sind Fragen hinsichtlich der strukturierten Speicherung und künftigen Nutzung von Daten noch offen. Einige Aspekte der Digitalisierung wurden im Rahmen des 63. DAfStb-Forschungskolloquiums (Tagungsband: DOI 10.26272/opus4-61338) in Vorträgen und Veröffentlichungen aufgegriffen und werden im Folgenden zusammengefasst. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Digitalisierung KW - Infrastruktur KW - Structural Health Monitoring KW - Digitaler Zwilling PY - 2025 SN - 0005-9846 VL - 75 IS - 4 SP - 136 EP - 138 PB - concrete content UG CY - Schermbeck AN - OPUS4-63070 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - JOUR A1 - Omidalizarandi, M. A1 - Herrmann, Ralf A1 - Kargoll, B. A1 - Marx, S. A1 - Paffenholz, J. A1 - Neumann, I. T1 - A validated robust and automatic procedure for vibration analysis of bridge structures using MEMS accelerometers N2 - Today, short- and long-term structural health monitoring (SHM) of bridge infrastructures and their safe, reliable and cost-effective maintenance has received considerable attention. From a surveying or civil engineer’s point of view, vibration-based SHM can be conducted by inspecting the changes in the global dynamic behaviour of a structure, such as natural frequencies (i. e. eigenfrequencies), mode shapes (i. e. eigenforms) and modal damping, which are known as modal parameters. This research work aims to propose a robust and automatic vibration analysis procedure that is so-called robust time domain modal parameter identification (RT-MPI) technique. It is novel in the sense of automatic and reliable identification of initial eigenfrequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters of a bridge structure using low numbers of cost-effective micro-electro-mechanical systems (MEMS) accelerometers. To estimate amplitude, frequency, phase shift and damping ratio coefficients, an observation model consisting of: (1) a damped harmonic oscillation model, (2) an autoregressive model of coloured measurement noise and (3) a stochastic model in the form of the heavy-tailed family of scaled t-distributions is employed and jointly adjusted by means of a generalised expectation maximisation algorithm. Multiple MEMS as part of a geo-sensor network were mounted at different positions of a bridge structure which is precalculated by means of a finite element model (FEM) analysis. At the end, the estimated eigenfrequencies and eigenforms are compared and validated by the estimated parameters obtained from acceleration measurements of high-end accelerometers of type PCB ICP quartz, velocity measurements from a geophone and the FEM analysis. Additionally, the estimated eigenfrequencies and modal damping are compared with a well-known covariance driven stochastic subspace identification approach, which reveals the superiority of our proposed approach. We performed an experiment in two case studies with simulated data and real applications of a footbridge structure and a synthetic bridge. The results show that MEMS accelerometers are suitable for detecting all occurring eigenfrequencies depending on a sampling frequency specified. Moreover, the vibration analysis procedure demonstrates that amplitudes can be estimated in submillimetre range accuracy, frequencies with an accuracy better than 0.1 Hz and damping ratio coefficients with an accuracy better than 0.1 and 0.2 % for modal and system damping, respectively. KW - Vibration analysis KW - Automatic modal parameters identification KW - MEMS KW - FEM analysis KW - Bridge monitoring PY - 2020 UR - https://www.degruyter.com/view/journals/jag/14/3/article-p327.xml DO - https://doi.org/10.1515/jag-2020-0010 SN - 1862-9016 VL - 14 IS - 3 SP - 1 EP - 28 PB - De Gruyter CY - Berlin AN - OPUS4-51338 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ramasetti, Eshwar Kumar A1 - Herrmann, Ralf A1 - Degener, Sebastian A1 - Baeßler, Matthias T1 - Development of generic AI models to predict the movement of vehicles on bridges N2 - For civil, mechanical, and aerospace structures to extend operation times and to remain in service, structural health monitoring (SHM) is vital. SHM is a method to examining and monitoring the dynamic behavior of essential constructions. Because of its versatility in detecting unfavorable structural changes and enhancing structural dependability and life cycle management, it has been extensively used in many engineering domains, especially in civil bridges. Due to the recent technical developments in sensors, high-speed internet, and cloud computing, data-driven approaches to structural health monitoring are gaining appeal. Since artificial intelligence (AI), especially in SHM, was introduced into civil engineering, these modern and promising methods have attracted significant research attention. In this work, a large dataset of acceleration time series using digital sensors was collected by installing a structural health monitoring (SHM) system on Nibelungen Bridge located in Worms, Germany. In this paper, a deep learning model is developed for accurate classification of different types of vehicle movement on the bridge from the data obtained from accelerometers. The neural network is trained with key features extracted from the acceleration dataset and classification accuracy of 98 % was achieved. KW - Structural Health Monitoring KW - Artifical Intelligence KW - Machine Learning KW - Nibelungen Bridge PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-620289 DO - https://doi.org/10.1016/j.prostr.2024.09.307 VL - 64 SP - 557 EP - 564 PB - Elsevier B.V. AN - OPUS4-62028 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Baeßler, Matthias T1 - Bestandteile Digitaler Zwillinge im Erhaltungsmanagement von Verkehrsbrücken T1 - Components of digital twins in the operation and maintenance management of traffic bridges N2 - Digitale Zwillinge werden zukünftig ein integraler Bestandteil des Erhaltungsmanagements von Verkehrsbrücken sein. In diesem Beitrag wird argumentiert, dass sie nicht nur als digitale Abbilder physikalischer Bauwerke verstanden werden sollten, sondern als eine umfassende digitale Methode, die durch die Integration von Datenerfassung, Erhaltungsmaßnahmen, Datenmanagement, Bauwerksbewertung und Entscheidungsunterstützung die Bauwerksüberwachung und ‐erhaltung verbessert. In diesem Zusammenhang wird betont, dass der Übergang von der reaktiven zur prädiktiven Erhaltung durch den Einsatz von Digitalen Zwillingen nur dann realisierbar ist, wenn neben den erforderlichen diagnostischen und prognostischen Zustandsanalysen auch Methoden zur Optimierung von Entscheidungen über Datenerfassung und Erhaltungsmaßnahmen implementiert werden. Zur Veranschaulichung der Diskussion werden in diesem Beitrag exemplarisch zwei Bestandteile eines Digitalen Zwillings für das Erhaltungsmanagement von Verkehrsbrücken am Beispiel einer Eisenbahnbrücke demonstriert. Dabei wird zum einen gezeigt, wie Monitoringdaten mittels eines Datenmanagementsystems strukturiert verwaltet und für angeknüpfte Analysen bereitgestellt werden. Zum anderen erfolgt im Rahmen einer bauwerksspezifischen Einwirkungsermittlung eine Zugidentifikation anhand von gemessenen Schwellenschwingungen. N2 - Digital twins will become an integral part of the operation and maintenance management of traffic bridges in the future. This paper argues that they should not only be understood as digital representations of physical structures but as a digital methodology that enhances the operation and maintenance of bridges through the integration of data collection, maintenance actions, data management, structural assessment, and decision support. In this context, it is emphasized that the transition from reactive to predictive maintenance using digital twins can only be achieved if, in addition to the necessary diagnostic and prognostic condition analyses, methods for optimizing decisions on data collection and maintenance actions are also implemented. To illustrate this discussion, two key components of a digital twin for the operation and maintenance management of traffic bridges are demonstrated using a railway bridge as an example. First, it is shown how monitoring data can be systematically managed and made available for subsequent analyses through a data management system. Second, train identification based on measured sleeper vibrations is conducted as part of an object-specific load assessment. KW - Digitale Zwillinge KW - Erhaltung KW - Inspektion KW - Monitoring KW - Brücken PY - 2025 DO - https://doi.org/10.1002/bate.202400101 SN - 1437-0999 SP - 1 EP - 11 PB - Ernst & Sohn CY - Berlin AN - OPUS4-62837 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 -