TY - CONF A1 - Herrmann, Ralf T1 - AISTEC 5. Verbundtreffen - Messfahrt auf der Maintalbrücke Gemünden N2 - Der Vortrag stellt die Durchführung und die ersten Ergebnisse der Belastungsfahrten der BAM im Rahmen des AISTEC Projekts an der Maintalbrücke am 19.05. und 20.05.2021 vor. Es wurden Tragwerksreaktionen des Bauwerks mit dem installierten Dauermonitoringsystem und insbesondere dem Betongelenk mit zusätzlich installierter Sensorik aufgezeichnet. Für die Ermittlung der Lastposition wurden mehrere Verfahren eingesetzt und verglichen. T2 - AISTEC 5. Verbundtreffen CY - Online meeting DA - 24.06.2021 KW - AISTEC KW - Structural Health Monitoring KW - Einflusslinie KW - Messfahrt KW - Belastungszug PY - 2021 AN - OPUS4-52927 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pirskawetz, Stephan A1 - Thiele, Marc A1 - Löhr, Manuel A1 - Loewe, Anna A1 - Degener, Sebastian A1 - Herrmann, Ralf T1 - Acoustic emission for monitoring of fatigue damage in concrete elements of wind turbine towers N2 - Wind energy has become an important player in the energy transition in Germany. Towers of onshore wind turbines are often designed as hybrid structures: the lower part is made of prestressed concrete whereas the upper part is made of steel tubes. The tall structures are permanently subjected to cyclic loads. A research project of BAM as part of the joint project WinConFat - Structure focusses on the evelopment of techniques to monitor fatigue damage evolution in the concrete part. Results of a previous project show that a combination of ultrasonic and acoustic emission testing can give an indication for critical conditions near the end of the fatigue life of the concrete. In the ongoing project acoustic emission sensors have been installed at the base and at the transition piece between concrete and steel of a hybrid wind turbine tower. Beside of acoustic emission measurement the sensor spacing allows for measuring the concrete ultrasonic velocity along the circumference in both levels. Additional measurements like strain, temperature, inclination, or acceleration allow for comparison of environmental loads and change of acoustic properties of the concrete. The paper focusses on first acoustic measurements recorded since December 2023 in comparison to operating data of the wind turbine. T2 - EWGAE2024: 36th Conference of the European Working Group on Acoustic Emission CY - Potsdam, Germany DA - 18.09.2024 KW - Concrete fatigue monitoring KW - Acoustic emission KW - Ultrasound KW - Hybrid wind turbine tower PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612197 DO - https://doi.org/10.58286/30234 SN - 1435-4934 VL - 29 IS - 10 SP - 1 EP - 9 AN - OPUS4-61219 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 - CONF A1 - Schneider, Ronald T1 - A risk-based framework for optimizing inspection and maintenance of deteriorating structures monitored by vibration-based monitoring systems N2 - In this presentation, a framework for integrating vibration-based structural health monitoring data into the optimization of inspection and maintenance of deteriorating structural systems is presented. The framework is demonstrated in an illustrative example considering a steel frame subject to fatigue. T2 - EERA JP Wind WORKSHOP on Ongoing research in offshore wind structures CY - Online meeting DA - 16.09.2021 KW - Structural Health Monitoring KW - Inspection KW - Maintenance KW - Deterioration KW - Vibration KW - Structural Systems PY - 2021 AN - OPUS4-53749 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Agasty, Amit T1 - A Preliminary Study on the Scaling of RC Structures under Blasting Loading N2 - Current capabilities for full-scale field blast testing are highly resource intensive. Reliable small-scale experiments are an effective alternative. Characterization of the dynamic response and damage of RC elements to scaled blast loads was investigated in scaled-down field experiments. Spatially resolved information on the dynamic structural response to blast loading was obtained using distributed fiber optic acoustic sensing (DAS), acceleration sensors as well as piezoelectric pressure sensors. T2 - 46. Informationstagung Sprengtechnik CY - Siegen, Germany DA - 25.04.2025 KW - Explosives KW - Blast and scaling effects KW - Concrete PY - 2025 AN - OPUS4-62994 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zinas, Orestis T1 - A Modular Gaussian Process Regression Toolbox for Uncertainty Aware Geotechnical Site Characterization N2 - A modular Gaussian Process Regression toolbox for efficient large-scale geotechnical site characterization from sparse 1D data was presented at the Third Future of Machine Learning in Geotechnics (3FOMLIG), Florence, Italy, October 16, 2025. The PyTorch/GPyTorch-based framework enables multivariate modeling of correlated soil properties and joint regression-classification of continuous CPT parameters with categorical soil units through Dirichlet transformations. Stochastic Variational Inference reduces computational complexity from O(N³) to O(M³), enabling GPU-accelerated processing of 100,000+ measurements. Validated on a 33 km² North Sea offshore wind farm site with 100+ sparse investigation points, the toolbox generates uncertainty-aware 3D predictions, supporting univariate, multivariate (LMC), and sequential multi-group modeling strategies. T2 - Third Future of Machine Learning in Geotechnics (3FOMLIG) CY - Florence, Italy DA - 15.10.2025 KW - Probabilistic site-characterization KW - Gaussian process regression KW - Bayesian inference KW - Offshore Wind Farms PY - 2025 AN - OPUS4-64423 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Auersch, Lutz T1 - A mid-frequency component of train-induced ground vibration due to scattered axle impulses and the irregularities of the soil and ballast N2 - The passage of the train is dominated by the impulses of the static axle loads. The response of the regular homogeneous and irregular soils has been calculated by the finite-element method in frequency domain. The superposition of the impulse responses yields the quasi-static component of the ground vibration which is restricted to very low frequencies and to the close near-field of the track. In case of an irregular soil or ballast of which the stiffness varies randomly in space, a mid-frequency ground vibration component is generated by the scattering of the axle impulses. Measurements will be shown which prove the existence of the mid-frequency ground vibration component and the unique explanation by the scattered axle impulses: many international measurements with a raised mid-frequency component, axle-box measurements with a too low mid-frequency dynamic load, amplitude-speed dependencies which are incompatible with irregularity-induced dynamic loads, and ground vibration reductions due to stiff track elements. T2 - International Conference on Noise and Vibration Engineering (ISMA/USD 2020) CY - Online meeting DA - 07.09.2020 KW - Ground vibration KW - Railway KW - Irregular soil KW - Irregular ballast KW - Axle impulses PY - 2020 AN - OPUS4-51212 LA - eng 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 - CONF A1 - Herrmann, Ralf 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, Germany DA - 10.06.2024 KW - Structural Health Monitoring (SHM) KW - SPP100+ KW - Nibelungen Bridge KW - Sensor technology PY - 2024 AN - OPUS4-61284 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Eichner, Lukas A1 - Gerards, Paul A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias T1 - A framework for data and structural integrity management for support structures in offshore wind farms based on building information modelling N2 - Support structures of wind turbines in German offshore wind farms are regularly inspected. Currently, inspection outcomes are generally not systematically recorded. This prevents their consistent handling and processing, which is a key requirement to enable an efficient structural integrity management. As part of the DiMoWind-Inspect project, a data model and reference designation system for such inspection results is developed to facilitate their identification, localization, quantification, tracing and linking throughout the lifetime of a wind farm and beyond. The inspection results together with structural health monitoring results and information on repairs form the basis for assessing and predicting the structural condition, estimating the remaining lifetime, and planning of future inspections, structural health monitoring, and repairs. As a basis for developing a digital structural integrity management, a generic framework is proposed in this paper, which describes the stakeholders, data, models, processes and workflows of the integrity management of support structures in offshore wind farms and their interrelations. The framework adopts a building information modelling approach to describe the information relevant to the structural integrity management. The central digital space of the framework is a common data environment. An implementation of the framework will enable a digital structural integrity management in which inspection, structural health monitoring, repair, assessment and planning outcomes generated throughout the life cycle of a wind farm can be consistently collected, managed, shared and processed by the relevant shareholders. T2 - 8th International Symposium on Reliability Engineering and Risk Management CY - Hannover, Germany DA - 04.09.2022 KW - Offshore wind energy KW - support structures KW - structural health monitoring KW - building information modelling KW - structural integrity management PY - 2022 DO - https://doi.org/10.3850/978-981-18-5184-1_MS-04-161-cd SP - 111 EP - 117 PB - Research Publishing CY - Singapore AN - OPUS4-55656 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -