TY - CONF A1 - Thöns, S. A1 - Schneider, Ronald A1 - Faber, M.H. ED - Haukaas, T. T1 - Quantification of the value of structural health monitoring information for fatigue deteriorating structural systems N2 - This paper addresses the quantification of the value of structural health monitoring (SHM) before its implementation for structural systems on the basis of its Value of Information (VoI). The value of SHM is calculated utilizing the Bayesian pre-posterior decision analysis modelling the structural life cycle performance, the integrity management and the structural risks. The relevance and precision of SHM information for the reduction of the structural system risks and the expected cost of the structural integrity management throughout the life cycle constitutes the value of SHM and is quantified with this framework. The approach is focused on fatigue deteriorating structural steel systems for which a continuous resistance deterioration formulation is introduced. In a case study, the value of SHM for load monitoring is calculated for a Daniels system subjected to fatigue deterioration. The influence of and the value of SHM in regard to the structural system risks and the integrity management is explicated and explained. The results are pointing to the importance of the consideration of the structural system risks for the quantification of the value of SHM. T2 - ICASP 12 - 12th International conference on applications of statistics and probability in civil engineering CY - Vancouver, Canada DA - 12.07.2015 PY - 2015 DO - https://doi.org/10.14288/1.0076284 SP - Paper 605, 1 EP - 8 AN - OPUS4-34332 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? 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 - TY - CONF A1 - Hille, Falk A1 - Herrmann, Ralf A1 - Schneider, Ronald A1 - Pitters, S. A1 - Wedel, F. A1 - Hindersmann, I. T1 - Developing a guideline for structural health monitoring of road bridges in Germany N2 - In recent years, Structural Health Monitoring (SHM) has become a useful and increasingly widely used tool for supporting lifetime extensions of existing bridges with known structural deficiencies or indications of potentially critical damages or damage processes. At the same time, methods and tools are emerging, which enable monitoring-informed predictive maintenance of new and existing bridges based on digital twins. The monitoring process – starting from the definition of monitoring actions and ending with decisions based on monitoring outcomes – is complex and requires expertise in structural engineering, operation and maintenance of bridges, metrology, and data analytics. To support German road authorities, engineering consultancies, building contractors and other stakeholders of the bridge management, the Federal Highway Research Institute (BASt) has initiated the development of a new guideline for applying SHM as part of the management of road bridges. The guideline will present various use cases and for each identified use case, it will propose a proven monitoring scheme. In addition, the guideline will provide guidance on assessing the benefits of SHM as well as a common approach to managing monitoring data as a systematic basis for integrating monitoring data in the bridge management. This contribution discusses the motivation, objectives, and scope of the guideline, describes its use case centric structure and outlines the proposed data management. T2 - IABMAS 2024 CY - Kopenhagen, Danmark DA - 24.06.2024 KW - Guideline KW - Structural health monitoring KW - Road bridges KW - Infrastructure PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613947 SN - 978-1-032-77040-6 DO - https://doi.org/10.1201/9781003483755-236 SP - 2009 EP - 2017 PB - CRC Press AN - OPUS4-61394 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Herrmann, Ralf A1 - Hille, Falk A1 - Pitters, S. A1 - Ramasetti, Eshwar Kumar A1 - Schneider, Ronald A1 - Wedel, F. A1 - Hindersmann, I. ED - Rogge, Andreas ED - Meng, Birgit T1 - Föderiertes Datenmanagement von Monitoringdaten aus Structural Health Monitoring Anwendungen und daraus gewonnenen Trainingsdaten bei Spannbetonbrücken N2 - Beim Structural Health Monitoring entstehen eine Vielzahl von Daten, Metadaten und Modellen mit hohem Wert für die Beurteilung des Bauwerkszustands und der Prognose von Veränderungen. Kontinuierlich anwachsende Datenbestände müssen nachhaltig geteilt, genutzt und über die Bauwerkslebensdauer archiviert werden. Dabei stellen die Vielfalt der Messaufgaben, die Heterogenität der Daten, die dezentrale Erfassung und z. T. der Umfang eine Herausforderung für die beteiligten Akteure dar. Für den Datentransfer der Monitoringdaten zwischen der Straßenbauverwaltung und den Monitoringausführenden wird ein flexibles Abstimmungsverfahren vorgeschlagen und ein Lösungskonzept für ein föderiertes Datenmanagement skizziert. Eine weitere Herausforderung stellt die bauwerksübergreifende Zusammenstellung von Trainingsdaten für konkrete KI-Anwendungen dar. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Structural Health Monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612902 SN - 978-3-9818564-7-7 SP - 178 EP - 185 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61290 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hille, Falk A1 - Herrmann, Ralf A1 - Pitters, S. A1 - Hindersmann, I. A1 - Schneider, Ronald A1 - Wedel, F. ED - Rogge, Andreas ED - Meng, Birgit T1 - Leitfaden für den strategischen Einsatz von Monitoring an Straßenbrücken N2 - Für die Anwendung von Structural Health Monitoring (SHM) im Rahmen des Managements von Straßenbrü-cken wurde zur Unterstützung von Straßenbauverwaltungen, Ingenieurbüros, Bauunternehmen und weiteren Akteuren die Erstellung eines Leitfadens initiiert. Dieser Leitfaden stellt typische Anwendungsfälle für Monito-ring vor und schlägt für diese bewährte Herangehensweisen vor. Darüber hinaus bietet der Leitfaden Empfeh-lungen für Ausschreibung und Vergabe von Monitoringleistungen, eine Anleitung zur Bewertung des wirt-schaftlichen Nutzens von SHM, Hinweise zur optimal einsetzbaren Messtechnik sowie einen Ansatz für das Datenmanagement zur Integration der Monitoringdaten in das Brückenmanagement. T2 - 11. Jahrestagung des DAfStb mit 63. Forschungskolloquium der BAM Green Intelligent Building CY - Berlin, Germany DA - 16.10.2024 KW - Leitfaden KW - Monitoring KW - Straßenbrücke PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612931 SN - 978-3-9818564-7-7 SP - 186 EP - 191 PB - Bundesanstalt für Materialforschung und -prüfung (BAM) CY - Berlin AN - OPUS4-61293 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zorn, Sebastian A1 - Unger, Jörg F. A1 - Schneider, Ronald ED - Silva Gomes, J. F. ED - Meguid, S. A. T1 - A probabilistic method for identification of vehicle loads and system parameters for reinforced concrete bridges N2 - Growth of vehicle traffic density can be observed in many countries all over the world. This accretion is caused by world-wide population growth on the one hand, but also by increasing freight volumes and, thus, freight transports on the streets on the other hand. This increased exposure becomes more and more of a problem for the civil infrastructure such as bridges. Many of these bridges are nowadays stressed to their limits by higher loads than they were originally designed for and/or operating times beyond the initially predicted life span. This raises questions about structural safety and lifetime prediction, of course, and therefore illustrates the need for accurate structural monitoring. Since the lifetime of bridge structures is primarily influenced by their traffic loading, an accurate identification of load configurations over the whole length of a structure is most desirable. In this paper, a method for vehicle load identification is proposed. It involves Bayesian Analysis and (quasi-)static importance functions in order to estimate vehicle positions, velocities and weights. The structure is modeled with finite elements in order to generate model predictions for different load configurations. The model predictions are compared to the actual measured data to identify the most probable loading configuration for that measurement. This involves the use of enhanced Monte Carlo simulations such as MCMC to reduce the computational effort. The measured data from different kinds of sensors can (and should) be combined for accuracy gain – in this case a combination of measured displacements and inclinations. Since the measurements take place over some time during the passage of the vehicle, these estimations are carried out for several time instants for which the estimation is carried out. The advantage of using Bayesian Updating Method is the embodied learning effect leading to an improvement of the estimation when adding new information in a new calculation step. Using the estimates for the loading conditions of a bridge structure as well as measurements of the structural responses, Bayesian analysis is again used in order to estimate localized structural parameters such as Young's modulus or Moments of Inertia in form of probability density functions yielding most probable values for the parameters. Considering the difficulties for load identification close to the support poles of the bridge and therefore for the proposed structural parameter identification procedure, it is clear that this problem is ill posed. Bayesian regularization methods also have proven to be very effective when handling ill posed problems. T2 - IRF2016 - Integrity Reliability Failure CY - Porto, Portugal DA - 24.07.2016 KW - Vehicle load identification KW - Structural health monitoring KW - Bayesian updating method PY - 2016 SN - 978-989-98832-4-6 VL - 2016 SP - PAPER REF: 6294-575 EP - 576 PB - INEGI-Instituto de Ciencia e Inovacao em Engenharia Mecanica e Gestao Industrial CY - Porto - Portugal AN - OPUS4-39833 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schneider, Ronald A1 - Thöns, S. A1 - Fischer, J. A1 - Bügler, M. A1 - Borrmann, A. A1 - Straub, D. T1 - A software prototype for assessing the reliability of a concrete bridge superstructure subjected to chloride-induced reinforcement corrosion N2 - A software prototype is developed for assessing and updating the reliability of single-cell prestressed concrete box girders subjected to chloride-induced reinforcement corrosion. The underlying system model consists of two integrated sub-models: a condition model for predicting the deterioration state of the box girder and a structural model for evaluating the overall system reliability. The condition model is based on a dynamic Bayesian network (DBN) model which considers the spatial variation of the corrosion process. Inspection data are included in the calculation of the system reliability through Bayesian updating on the basis of the DBN model. To demonstrate the effect of partial inspections, the software prototype is applied to a case study of a typical highway bridge with six spans. The case study illustrates that it is possible to infer the condition of uninspected parts of the structure due to the spatial correlation of the corrosion process. T2 - IALCCE 2014 - 4th International symposium on life-cycle civil engineering CY - Tokyo, Japan DA - 16.11.2014 PY - 2015 SN - 978-1-138-00120-6 SP - 846 EP - 853 PB - Taylor & Francis CY - London AN - OPUS4-32220 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 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 - 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 - 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 -