TY - JOUR A1 - Wedel, F. A1 - Pitters, S. A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Hindersmann, I. T1 - Guideline for the strategic application of monitoring of road bridges in Germany N2 - Engineering structures are an important part of our transport infrastructure. Their failure is associated with high safety risks and economic damage. Ensuring the availability of these constructions and guaranteeing their operational safety are thus important tasks. Currently, maintenance of engineering structures is a reactive process and therefore not yet a predictive process. Every 3 to 6 years, the structures are visually inspected, all damages are documented and a condition grade is assigned to the structure based on the detected damages. Continuous data-providing methods such as monitoring are used only very occasionally. With the help of monitoring, condition data about the structures are continuously collected, which can be used for a better assessment of the structures. On the one hand, this would increase safety of engineering structures, and on the other hand, it makes the shift towards predictive maintenance management based on real-time data and predictions, where the onset of damage is detected before it occurs, possible in the first place. Monitoring plays therefore an important role in the management of the infrastructure. It is a crucial a step towards the digitalisation of our infrastructure and existing processes. Although there are many use cases for monitoring that have already been successfully implemented, monitoring is not yet widely used by German road authorities due to several challenges that were also identified within the scope of the project. Therefore, a guide has been developed in which the current obstacles related to monitoring are identified and possibilities for integrating monitoring into existing processes are offered. To ensure optimal alignment with the needs of road authorities, both an online survey and a workshop have been conducted. The results are summarised in a brochure that is made available to authorities to improve the use of monitoring in Germany. This paper presents the guideline and the brochure. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam, Germany DA - 10.06.2024 KW - Verkehrsinfrastrukturen KW - Engineering Structures KW - Bridges KW - Structural Health Monitoring KW - Guideline PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612821 UR - https://www.ndt.net/search/docs.php3?id=29582 DO - https://doi.org/10.58286/29582 SN - 1435-4934 VL - 29 IS - 7 SP - 1 EP - 8 PB - NDT.net GmbH & Co. KG CY - Mayen AN - OPUS4-61282 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 - 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 - Lauhoff, Christian A1 - Degener, Sebastian A1 - Bolender, Artjom A1 - Liehr, Alexander A1 - Hübner, Leoni A1 - Frenck, Johanna-Maria A1 - Molotnikov, Andrey A1 - Niendorf, Thomas T1 - In Situ Synchrotron Diffraction Assessment of Reversibility of the Martensitic Transformation in Single-Crystalline Co–Ni–Ga Shape Memory Alloy Under Torsion N2 - AbstractHeusler-type Co–Ni–Ga shape memory alloys attracted significant attention due to their excellent functional properties in single-crystalline state under both compressive and tensile loading. The present study investigates the superelastic deformation behavior under torsion. Using a newly installed torsion testing setup, in situ synchrotron diffraction was carried out on single-crystalline material in order to investigate the martensitic phase transformation. Incremental deformation experiments reveal a fully reversible martensitic transformation under torsional loading at room temperature, leading to excellent strain recovery after deformation to 6.5% shear strain. Furthermore, relevant aspects towards the analysis of powder diffraction data obtained for single-crystalline material in transmission mode under torsional loading are presented and critically discussed. KW - Shape memory alloy KW - Pseudoelasticity KW - Martensitic transformation KW - Synchrotron diffraction KW - In situ testing KW - Torsion PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-621872 DO - https://doi.org/10.1007/s40830-024-00496-8 VL - 10 SP - 326 EP - 333 PB - Springer Science and Business Media LLC AN - OPUS4-62187 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Auersch, Lutz T1 - Prediction of ground vibrations from rail tunnels –finite-element, boundary-element and wavenumber calculations N2 - The prediction of ground and building vibrations has been established for surface lines and has now been extended to tunnel lines. The wave propagation in homogeneous or layered soils (the transmission) is calculated by an integration in wavenumber domain. The wave amplitudes at different distances and for different frequencies will be analysed for the following situations. 1. The horizontal propagation from a surface point to a surface point constitutes the basic rules. 2. The horizontal propagation from a source point at depth to a receiver point at depth which is related to a building with a deep basement or on a pile foundation. 3. The propagation from depth to the surface, which is the normal case for free-field measurements, has some different characteristics, for example a weaker attenuation with the horizontal distance from the source, which can be approximated by the full-space solution and the reflection rules for incident waves. The emission from a tunnel structure has been calculated by a finite-element model of the tunnel combined with a boundary-element model of the soil giving the reduction compared to a point-load excitation. The immission has been analysed by finite-element models of tunnel-soil-building systems for examples of research and consultancy work. Measurement results from a high-speed and a metro line confirm some of the established rules. KW - Railway tunnel KW - Ground vibration KW - Building vibration KW - Wavenumber integral KW - Full-space solution KW - Reflection at the surface PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627015 DO - https://doi.org/10.1088/1742-6596/2909/1/012013 SN - 1742-6596 VL - 2909 SP - 1 EP - 12 PB - IOP Publishing CY - London AN - OPUS4-62701 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -