TY - CONF A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias A1 - El-Athman, Rukeia T1 - Research Data Management of Structural Health Monitoring Projects and Subsequent Applications of Artificial Intelligence Methods N2 - Structural health monitoring (SHM) intends to improve the management of engineering structures. The number of successful SHM projects – especially SHM research projects – is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. Data and metadata from monitoring the structural health of the Maintalbrücke Gemünden for a period of 16 months are managed with the RDM system BAM Data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system. T2 - 11th International Conference on Bridge Maintenance, Safety and Management (IABMAS2022) CY - Barcelona, Spain DA - 11.07.2022 KW - Research data management KW - Structural health monitoring KW - Artificial intelligence PY - 2022 SN - 978-1-032-35623-5 SN - 978-1-003-32264-1 DO - https://doi.org/10.1201/9781003322641-127 SP - 1061 EP - 1068 PB - CRC Press CY - Boca Raton AN - OPUS4-55493 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hille, Falk A1 - Nerger, Deborah A1 - Moosavi, Robabeh A1 - Grunwald, Marcel T1 - Discussion on data evaluation of tomographic and numerical results N2 - The contribution discusses the processing and analysis of data generated on two different ways of investigations for impact damage in reinforced concrete structures. Damage investigations are essential to determine type and characteristics of damage and thus the residual capacity. Damage describing data is generated using two different types of investigation, a non-destructive tomographic as well as numerical examination. Subsequently, data of both sources was merged and analysed. Within the research project “Behaviour of structural components during impact load conditions caused by aircraft fuel tank collision” reinforces concrete plates were damaged by impact loading, see Hering (2020). Afterwards the damaged specimens were investigated tomographically as well as numerically using several methods and models. Aim of the presented research work was to specify an objective comparability of numerical data with experimentally determined damage patterns and based on this, to establish a quantitative damage evaluation. T2 - 26th International Conference on Structural Mechanics in Reactor Technology CY - Potsdam, Germany DA - 10.07.2022 KW - Impact damage on reinforced concrete KW - Tomographic damage evaluation KW - Numerical damage simulation PY - 2022 SP - 1 EP - 8 AN - OPUS4-55474 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hering, Marcus A1 - Fiedler, Georg A1 - Schubert, T. A1 - Hille, Falk A1 - Hüsken, Götz A1 - Beckmann, B. A1 - Grunwald, Marcel A1 - Redmer, Bernhard A1 - Rogge, Andreas T1 - Investigation of multiple impact-damaged reinforced concrete structures as a reference for liquid penetration behavior and tomographic studies N2 - The structural integrity of outer reinforced concrete (RC) containments of nuclear power plants provides an essential shield against external hazards. If this containment is damaged by an impact event, such as an aircraft crash, the question arises to which degree the reinforced concrete containment still has its protective capability. This concerns both purely structural protection and protection against liquids penetrating the interior of the containment. Due to the dimensions of the containment structures, it is difficult to perform real scale impact experiments, so in the past decades plate geometries at medium scale have been used for investigations. Detailed investigations on the structural behaviour of RC members or RC plates subjected to impact loading have already been presented in Just et al., Hering, Hering et al., Bracklow et al., Hille et al. and Nerger et al. The following investigations deal with the single and multiple impact event (first hard impact and/or subsequent soft impact) on a RC specimen, which provides the basis for further investigations. A description of the test setup and the test procedure as well as a presentation of the test results from the impact tests are provided. Furthermore, the experimental program is presented, which the damaged RC specimens are to undergo to deal with the question of how much the impact-damaged RC structure has become permeable to liquid media, such as water and kerosene, depending on the intensity of the impact. The aim of these following investigations is to develop a test setup that can be applied to investigate the liquid penetration behaviour (LPB) of small, medium, and large-scale RC members. In addition to the liquid penetration experiments, the damaged specimens are to be examined by planar tomography to obtain the damage inside the specimen. The combination of damaging event, fluid penetration behaviour and tomography should enable a comprehensive understanding of the damage to the RC specimen. T2 - Conference: 27th International Conference on Structural Mechanics in Reactor Technology (SMiRT) CY - Yokohama, Japan DA - 04.03.2024 KW - Drop tower KW - Hard impact KW - Soft impact KW - Multiple impact PY - 2024 SP - 1 EP - 10 PB - IASMiRT CY - Yokohama, Japan AN - OPUS4-59723 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Viefhues, Eva A1 - Said, Samir A1 - Herrmann, Ralf A1 - Baeßler, Matthias ED - Papadrakis, M. ED - Fragiadakis, M. ED - Papadimitriou, C. T1 - Vibration-based Structural Health Monitoring of a Reinforced Concrete Beam Subjected to Varying Ambient Conditions Using Bayesian Methods N2 - Inspection and maintenance contribute significantly to the lifetime cost of bridges. There is significant potential in using information obtained through structural health monitoring to update predictive models of the condition and performance of such structures, and thus ena-ble an improved decision-making regarding inspection and maintenance activities. Within the AISTEC project funded by the German Federal Ministry of Education and Research, we develop vibration-based structural health monitoring systems aimed at continuously provid-ing information on the structural condition of bridges. Environmental variations such as changing ambient temperatures can significantly influence the dynamic characteristics of bridges and thus mask the effect of structural changes and damages. It remains a challenge to account for such influences in structural health monitoring. To study the effect of ambient temperatures on the dynamic characteristics of beam structures, we monitor the vibration response of a reinforced concrete beam in the uncracked and cracked state at varying tem-peratures in a climate chamber. We postulate a set of competing parameterized probabilistic structural models, which explicitly account for the effect of varying ambient temperatures on the mechanical properties of the system. We then combine the information provided by the structural models with the information contained in the recorded vibration data to learn the parameters of the temperature-dependent structural models and infer the plausible state of the beam using Bayesian system identification and model class selection. T2 - EURODYN 2020, XI International Conference on Structural Dynamics CY - Online meeting DA - 23.11.2020 KW - Structural health monitoring KW - Bayesian methods KW - Environmental effects PY - 2020 SN - 978-618-85072-2-7 SN - 978-618-85072-0-3 SP - 1254 PB - Institute of Structural Analysis and Antiseismic Research, School of Civil Engineering, National Technical University of Athens (NTUA) CY - Athens, Greece AN - OPUS4-51590 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Hille, Falk A1 - Mevel, L. T1 - Fault detection for linear parameter varying systems under changes in the process noise covariance N2 - Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models -- each estimated from data measured in a reference state -- are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system. T2 - IFAC World Congress 2020 CY - Online meeting DA - 11.07.2020 KW - Linear parameter varying systems KW - Fault detection KW - Changing process noise KW - Subspace-based residual KW - Model interpolation PY - 2020 SP - 13858 AN - OPUS4-51617 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias ED - Yokota, H. ED - Frangopol, D. M. T1 - Bayesian system identification of a reinforced concrete beam subject to temperature variations based on static response data N2 - Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data. T2 - Tenth International Conference on Bridge Maintenance, Safety and Management (IABMAS 2020) CY - Online meeting DA - 11.04.2021 KW - Structural systems KW - Bayesian system identification KW - Damage identification KW - Environmental effects KW - Structural health monitoring PY - 2021 DO - https://doi.org/10.1201/9780429279119-125 SP - 934 EP - 941 PB - CRC Press AN - OPUS4-52809 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Viefhues, Eva A1 - Döhler, M. A1 - Simon, Patrick A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Mevel, L. ED - Cunha, A. ED - Caetano, E. T1 - Stochastic subspace-based damage detection of a temperature affected beam structure N2 - Structural health monitoring (SHM) of civil structures often is limited due to changing environmental conditions, as those changes affect the structural dynamical properties in a similar way like damages can do. In this article, an approach for damage detection under changing temperatures is presentedand applied to a beam structure. The used stochastic subspace-based algorithm relies on a reference null space estimate, which is confronted to data from the testing state in a residual function. For damage detection the residual is evaluated by means of statistical hypothesis tests. Changes of the system due to temperature effects are handled with a model interpolation approach from linear parameter varying system theory. From vibration data measured in the undamaged state at some few reference temperatures, a model of the dynamic system valid for the current testing temperature is interpolated. The reference null space and the covariance matrix for the hypothesis test is computed from this interpolated model. This approach has been developed recently and was validated in an academic test case on simulations of a mass-spring-damper. In this paper, the approach is validated experimentally on a beam structure under varying temperature conditions in a climate chamber. Compared to other approaches, the interpolation approach leads to significantly less false positive alarms in the reference state when the structure is exposed to different temperatures, while faults can still be detected reliably. T2 - 10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 10 CY - Online meeting DA - 30.06.2021 KW - Damage detection KW - Subspace methods KW - Temperature effects KW - Model interpolation KW - Climate chamber KW - Laboratory beam structure PY - 2021 SP - 1 EP - 6 AN - OPUS4-52999 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Simon, Patrick A1 - Schneider, Ronald A1 - Baeßler, Matthias A1 - Recknagel, Christoph ED - Cunha, Á. ED - Caetano, E. T1 - Enhancing structural models with material tests and static response data - a case study considering a steel beam with asphalt layer subject to temperature variations N2 - Gradual or sudden changes in the state of structural systems caused, for example, by deterioration or accidental load events can influence their load-bearing capacity. Structural changes can be inferred from static and/or dynamic response data measured by structural health monitoring systems. However, they may be masked by variations in the structural response due to varying environmental conditions. Particularly, the interaction of nominally load-bearing components with nominally non-load bearing components exhibiting characteristics that vary as a function of the environmental conditions can significantly affect the monitored structural response. Ignoring these effects may hamper an inference of structural changes from the monitoring data. To address this issue, we adopt a probabilistic model-based framework as a basis for developing digital twins of structural systems that enable a prediction of the structural behavior under varying ambient condition. Within this framework, different types of data obtained from real the structural system can be applied to update the digital twin of the structural system using Bayesian methods and thus enhance predictions of the structural behavior. In this contribution, we implement the framework to develop a digital twin of a simply supported steel beam with an asphalt layer. It is formulated such that it can predict the static response of the beam in function of its temperature. In a climate chamber, the beam was subject to varying temperatures and its static response wass monitored. In addition, tests are performed to determine the temperature-dependent properties of the asphalt material. Bayesian system identification is applied to enhance the predictive capabilities of the digital twin based on the observed data. T2 - International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-10) CY - Online meeting DA - 30.06.2021 KW - Digital twin KW - Structural health monitoring KW - Material tests KW - Bayesian updating PY - 2021 SN - 2564-3738 SP - 1537 EP - 1544 AN - OPUS4-54126 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Auersch, Lutz T1 - Railway vibration – fast physics-based models for the prediction of ground vibration and the identification of track damage N2 - The following applications of machine learning will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, physics-based machine learning for tunnel lines) 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, machine learning for layered soil) 3. Track damage detection and quantification from frequency response functions and moving load responses 4. Bridge damage detection and localisation from modal analysis and moving load 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. The prediction of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software however, fast calculations are needed. Several time-consuming detailed calculations have been used to develop simpler and fast models for the surface railway lines. The more challenging prediction from tunnel lines will be attacked by purely mathematical and by physics-informed machine learning. The dynamic stiffnesses of isolated or un-isolated railway tracks from detailed calculations with a continuous soil have been approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction roller) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance. T2 - 11th European Workshop on Structural Health Monitoring (EWSHM 2024) CY - Potsdam, Germany DA - 10.06.2024 KW - Vibration prediction KW - Track damage detection KW - Human and machine learning KW - Wave propagation KW - Surface line KW - Tunnel KW - Bridge resonance PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612462 DO - https://doi.org/10.58286/29865 SN - 1435-4934 SP - 1 EP - 9 PB - NDT.net CY - Kirchwald AN - OPUS4-61246 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hering, Marcus A1 - Agasty, Amit A1 - Costard, René A1 - Hüsken, Götz A1 - Chruscicki, Sebastian A1 - Hicke, Konstantin T1 - Explosion effects on reinforced concrete structures – A preliminary study of scaling laws N2 - Blast tests are required to investigate accidental or intentional blast events and to assess the level of protection for people and facilities in critical infrastructures. Conducting large-scale field tests for complex scenarios is very resource intensive. Reliable small-scale experiments are a promising alternative. However, the scaling laws for the design of reinforced concrete structures under blast loads are not sufficiently established. In our research work, a consortium made up of three BAM departments, focuses on reinforced concrete structures that are suitable for the standardization of scaled blast tests. As part of the feasibility study, blast tests were carried out on test specimens of different sizes on the BAM Test Site for Technical Safety (TTS). Various reinforced concrete plates were tested and subjected to different blast loads. The blast tests included various measurement techniques to quantify the blast load and the behavior of the reinforced concrete plate. The blast load was measured with flush-mounted piezoelectric pressure gauges, while accelerometers and embedded fiber optic sensor cables were used to characterize the dynamic behavior of the plate under blast loading. In addition, damage characteristics were also determined using distributed fiber optic sensing. The application of these measurement techniques as well as the use of different numerical software tools offer the possibility to verify or adapt the scaling laws. T2 - 19th International Symposium on Interaction of the Effects of Munitions with Structures (19th ISIEMS) CY - Bonn, Germany DA - 09.12.2024 KW - Scaling KW - Blast KW - Fiber optic sensing PY - 2024 SP - 1 EP - 9 AN - OPUS4-62258 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -