TY - CONF A1 - Gerards-Wünsche, Paul A1 - Ratkovac, Mirjana A1 - Schneider, Ronald A1 - Hille, Falk A1 - Baeßler, Matthias T1 - A framework for assessing the reliability of crack luminescence – an automated fatigue crack detection system T2 - SPIE Conference Smart Structures + Nondestructive Evaluation 2023 N2 - The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing. T2 - SPIE Conference Smart Structures + Nondestructive Evaluation 2023 CY - Long Beach, CA, USA DA - 24.03.2023 KW - Structural Health Monitoring KW - Non-Destructive Evaluation KW - Probability of Detection KW - Reliability KW - Artificial Intelligence KW - Computer Vision KW - Crack Luminescence KW - Wind PY - 2023 SN - 978-1-5106-6086-1 DO - https://doi.org/10.1117/12.2658390 SP - 1 EP - 15 AN - OPUS4-57244 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 T2 - Bridge safety, maintenance, management, life-cycle, resilience and sustainability - Proceedings of the eleventh International Conference on Bridge Maintenance, Safety and Management (IABMAS 2022), Barcelona, Spain, July 11-15, 2022 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 - 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 T2 - Proceedings of EURODYN 2020, XI International Conference on Structural Dynamics 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 - Eichner, Lukas A1 - Schneider, Ronald A1 - Simon, Patrick A1 - Baeßler, Matthias T1 - Optimal sensor placement for vibration-based structural health monitoring obtained via value of information analysis as part of a digital structural integrity management of offshore structures T2 - 3rd International Conference on Health Monitoring of Civil & Maritime Structures (HeaMES 2022) - Proceedings N2 - A digital structural integrity management of offshore structures enables an optimized planning of inspections and repairs with risk-based methods. In a risk-based approach, the inspection and repair strategy that minimizes the expected lifetime costs consisting of the expected inspection, repair and failure costs is determined. In addition to inspections, information on the structural condition can be continuously obtained by monitoring the vibration response of the structural system. Changes in the vibration characteristics quantified in terms of modal properties can be an indication of structural damage. In risk-based inspection and repair planning, the effect of monitoring results is determined via Bayesian updating of the structural condition and reliability. This information should be applied to inform decisions on inspections and may result in a reduced inspection effort. The benefit of continuously monitoring the structural health can be quantified in terms of the value of information, which corresponds to the difference between the expected lifetime costs with and without monitoring. In this work, we demonstrate in a numerical example how an optimized sensor placement for a vibration-based structural health monitoring system can be determined by maximizing the value of information. T2 - 3rd International Conference on Health Monitoring of Civil & Maritime Structures (HeaMES 2022) CY - Online meeting DA - 08.06.2022 KW - Structural Health Monitoring KW - Value of Information KW - Offshore Wind Energy KW - Optimal Sensor Placement KW - Bayesian System Identification PY - 2022 UR - https://www.researchgate.net/publication/362569906_Optimal_sensor_placement_for_vibration-based_structural_health_monitoring_obtained_via_value_of_information_analysis_as_part_of_a_digital_structural_integrity_management_of_offshore_structures SN - 978-1-8383226-7-0 SP - 23 EP - 32 AN - OPUS4-55465 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald T1 - Effect of repair models on risk based optimal inspection strategies for support structures of offshore wind turbines T2 - Proceedings of the 5th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures (SMAR 2019) N2 - Owners or operators of offshore wind farms perform inspections to collect information on the condition of the wind turbine support structures and perform repairs if required. These activities are costly and should be optimized. Risk-based methods can be applied to identify inspection and repair strategies that ensure an optimal balance between the expected total service life cost of inspection and repair, and the achieved risk reduction. Such an optimization requires explicit modeling of repairs. In this paper, the impact of different repair models on the results of a risk-based optimization of inspection and repair strategies is quantified in a numerical example considering a jacket-type steel frame subject to high-cycle fatigue. The example showed that, in this specific application, there is no need for detailed modeling of the behavior of repaired welded connections. T2 - 5th International Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures (SMAR 2019) CY - Potsdam, Germany DA - 27.08.2019 KW - Risk KW - Reliability KW - Inspection planning KW - Offshore wind turbines PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-488297 UR - https://www.smar2019.org/Portals/smar2019/bb/Th.2.A.4.pdf SP - Paper Th.2.A.4, 1 EP - 8 AN - OPUS4-48829 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Straub, D. ED - Matos, J.C. ED - Lourenço, P.B. ED - Oliveira, D.V. ED - Branco, J. ED - Proske, D. ED - Silva, R.A. ED - Sousa, H.S. T1 - Cumulative Failure Probability of Deteriorating Structures: Can It Drop? T2 - 18th International Probabilistic Workshop. IPW 2020. Lecture Notes in Civil Engineering N2 - The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic Engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability. Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model. T2 - 18th International Probabilistic Workshop (IPW 2020) CY - Online meeting DA - 12.05.2021 KW - Deterioration KW - Structural systems KW - Time-variant reliability KW - Bayesian updating KW - Inspection KW - Monitoring PY - 2021 DO - https://doi.org/10.1007/978-3-030-73616-3_18 VL - 153 SP - 253 EP - 264 PB - Springer CY - Cham, Switzerland AN - OPUS4-52769 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -