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Die BAM erforscht mit Partnern an der TUM und DTU Methoden und Modelle für ein optimiertes Tragwerksmanagement von Infrastrukturbauwerken. Im Rahmen des vom BMVI und der BASt aufgelegten Projektclusters „Intelligente Brücke“ wurde ein Software-Prototyps zur zuverlässigkeitsbasierten Bewertung eines konkreten Spannbetonüberbaus unter Berücksichtigung von Inspektionen und Überwachungssystemen entwickelt. Der entwickelte Software-Prototyp ermöglicht Benutzern ohne vertiefte Kenntnisse der Zuverlässigkeitstheorie eine Berechnung des Einflusses von Bauwerksinformationen auf den Systemschädigungszustand und die Tragsicherheit. Auf dieser Grundlage können effiziente Inspektions- und Überwachungsmaßnahmen identifiziert und das Erhaltungsmanagement optimiert werden.
Der vorliegende Bericht beschreibt Konzepte für eine intelligente Brücke auf der Grundlage einer zuverlässigkeitsbasierten Zustandsbewertung unter Berücksichtigung von Bauwerksinformationen, welche aus Prüfungen, Inspektionen und Überwachung gewonnen werden. Das Brückensystem wird durch ein Modell beschrieben, welches den zentralen Teil des Konzeptes darstellt. Das Modell wird in Schädigungsmodelle und ein Tragwerkssystem-Modell unterteilt. Dieses Modell wird a-priori durch die Eingangsdaten (welche etwa die Geometrie, die Materialien und die Verwendung der Brücke beschreiben) charakterisiert. Aus diesen ergeben sich dann auch die Ausgangsmodelle. Um die signifikanten Streuungen und Unsicherheiten adäquat abzubilden sind diese Modelle probabilistisch. Das Modell liefert eine sich kontinuierlich ändernde probabilistische Zustandsbewertung. Die Zustandsbewertung gibt eine Aussage über den Zustand und die Zuverlässigkeit des Brückensystems und seiner Bauteile und dient als Grundlage für die Planung und die Optimierung von Maßnahmen. Die Verwendung von Resultaten aus Inspektionen, Prüfungen und Überwachungen erfolgt durch eine Aktualisierung der Modellparameter. Die Aktualisierung beruht auf der Methode der Bayes'schen Aktualisierung und wird auf der Grundlage der entwickelten Klassifizierung der Bauwerksinformationen mit entsprechenden Methoden durchgeführt. Dieses Verfahren erlaubt es, alle Informationen in konsistenter Weise in ein einziges Modell einfließen zu lassen. Dabei wird die Genauigkeit und Aussagekraft der gewonnenen Daten und Beobachtungen explizit berücksichtigt. Durch die Aktualisierung der Modellparameter unter Berücksichtigung von Systemeffekten wird die Zustandsbewertung der Bauteile und des Brückensystems aktualisiert. Das ermöglicht die Planung und die Optimierung von Maßnahmen unter Berücksichtigung der Bauwerksinformationen. Auf diese Weise wird die intelligente Brücke mit Inspektionen und Überwachungen zu einem adaptiven System, welches sich Veränderungen anpassen kann.---------------------------------------------------------------------------------------------------------------------------------------------------------------------------This report describes concepts for an intelligent bridge on the basis of a reliability-based condition assessment utilizing inspection and monitoring data. The central part of the concept is a model describing the bridge system. The overall bridge model is comprised of deterioration models and a mechanical system model of the structure. The model is a-priori characterized by the input data (such as the geometry, the materials, and the utilization of the bridge). Significant uncertainties in the deterioration modeling as well as the load conditions motivate a probabilistic modeling. The model enables a continuously updated probabilistic condition assessment. It provides information about the condition and the reliability of the bridge system and its components and serves as a basis for the planning and the optimization of maintenance actions. Inspection, measurement, and monitoring information are included by updating the model parameters. Bayesian updating is utilized for the updating of the model parameters and is performed on the basis of the developed classification of structural information and the corresponding procedures. These methodologies allow to consistently incorporate all available information into the model and the prediction. Thereby, the precision and the validity of the data and observations are explicitly accounted for. The condition assessment of the bridge system and its components is updated by updating the model parameters, and maintenance actions can be planned and optimized taking into account all available information in a rational and consistent manner. In this way, the intelligent bridge with inspections, measurements, and monitoring information becomes an adaptive system, which has the ability to adjust to changes.
The Influence of brittle Daniels system characteristics on the value of load monitoring information
(2016)
This paper addresses the influence of deteriorating brittle Daniels system characteristics on the value of structural health monitoring (SHM). The value of SHM is quantified as the difference between the life cycle benefits with and without SHM. A value of SHM analysis is performed within the framework of the Bayesian pre-posterior decision theory and requires (1) structural performance modelling and prediction, (2) structural integrity management models, (3) the (pre-posterior) modelling of SHM and (4) the coupling of SHM and the structural performance models. The pre-posterior decision theoretical framework facilitates that the value of SHM can be quantified before the SHM system is quantified and before data are acquired. The results of this study support decisions to select structural systems for which the SHM strategy load monitoring is optimal.
An efficient approach to reliability analysis of deteriorating structural systems is presented, which considers stochastic dependence among element deterioration. Information on a deteriorating structure obtained through inspection or monitoring is included in the reliability assessment through Bayesian updating of the system deterioration model. The updated system reliability is then obtained through coupling the updated deterioration model with a probabilistic structural model. The underlying high-dimensional structural reliability problems are solved using subset simulation, which is an efficient and robust sampling-based algorithm suitable for such analyses. The approach is demonstrated in two case studies considering a steel frame structure and a Daniels system subjected to high-cycle fatigue.
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.
Inspection and maintenance of concrete bridges is a major cost factor in transportation infrastructure, and there is significant potential for using information gained during inspection to update predictive models of the performance and reliability of such structures. In this context, this paper presents an approach for assessing and updating the reliability of prestressed concrete bridges subjected to chloride-induced reinforcement corrosion. The system deterioration state is determined based on a Dynamic Bayesian Network (DBN) model that considers the spatial variability of the corrosion process. The overall system reliability is computed by means of a probabilistic structural model coupled with the deterioration model. Inspection data are included in the system reliability calculation through Bayesian updating on the basis of the DBN model. As proof of concept, a software prototype is developed to implement the method presented here. The software prototype is applied to a typical highway bridge and the influence of inspection information on the system deterioration state and the structural reliability is quantified taking into account the spatial correlation of the corrosion process. This work is a step towards developing a software tool that can be used by engineering practitioners to perform reliability assessments of ageing concrete bridges and update their reliability with inspection and monitoring data.
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.
Monitoringsysteme erfassen kontinuierlich Bauwerksdaten wie z.B. Bauwerksbeschleunigungen, auf deren Grundlage Bauwerksschäden mit Hilfe von SHM-Methoden quantifiziert werden können. Mit den gewonnenen Informationen über den aktuellen Bauwerkszustand können Vorhersagen des Bauwerkszustandes und der Bauwerkszuverlässigkeit aktualisiert und erforderliche Inspektionen und Instandhaltungsmaßnahmen vorausschauend geplant werden. Im BMBF-Forschungsvorhaben AISTEC entwickeln der Fachbereich 7.2 „Ingenieurbau“ innovative Monitoringverfahren zur Systemidentifikation und automatischen Detektion, Lokalisierung und Quantifizierung von Schäden an Infrastrukturbauwerken anhand von gemessenen dynamischen und statischen Bauwerksdaten. Im Rahmen dieses Projektes werden die Verfahren an der Maintalbrücke bei Gemünden angewendet, welche Teil der ICE-Strecke Hannover-Würzburg ist. In diesem Vortrag wird das für die Maintalbrücke Gemünden geplante und umgesetzte Monitoingsystem vorgestellt.
The current practice of operating and maintaining deteriorating structural systems ensures acceptable levels of structural reliability, but it is not clear how efficient it is. Changing the current prescriptive approach to a risk-based approach has great potential to enable a more efficient management of such systems. Risk-based optimization of operation and maintenance strategies identifies the strategy that optimally balances the cost for controlling deterioration in a structural system with the achieved risk reduction. Inspections and monitoring are essential parts of operation and maintenance strategies. They are typically performed to reduce the uncertainty in the structural condition and inform decisions on future operation and maintenance actions. In risk-based optimization of operation and maintenance strategies, Bayesian updating is used to include information contained in inspection and monitoring data in the prediction of the structural reliability. All computations need to be repeated many times for different potential inspection and monitoring outcomes. This motivates the development of robust and efficient approaches to this computationally challenging task.
The reliability of deteriorating structural systems is time-variant because the loads on them and their capacities change with time. In most practical applications, the reliability analysis of deteriorating structural systems can be approached by dividing their lifetime into discrete time intervals. The time-variant reliability problem can then be represented by a series of time-invariant reliability problems. Using this methodology as a starting point, this thesis proposes a novel approach to compute the time-variant reliability of deteriorating structural systems for which inspection and monitoring data are available. The problem is formulated in a nested way in which the prediction of the structural condition is separated from the computation of the structural reliability conditional on the structural condition. Information on the structural condition provided by inspections and monitoring is included in the reliability assessment through Bayesian updating of the system deterioration model employed to predict the structural condition. The updated system reliability is obtained by coupling the updated deterioration model with a probabilistic structural model utilized to calculate the failure probability conditional on the structural condition. This approach is the first main outcome of this thesis and termed nested reliability analysis (NRA) approach. It is demonstrated in two numerical examples considering inspected and monitored steel structures subject to high-cycle fatigue.
An alternative – recently developed – approach, which also follows the strategy of discretizing time, describes deteriorating structural systems with hierarchical dynamic Bayesian networks (DBN). DBN combined with approximate or exact inference algorithms also enable the computation of the time-variant reliability of deteriorating structural systems conditional on information provided by inspection and monitoring data. In this thesis – as a proof of concept – a software prototype is developed based on the DBN approach, which can be used to assess the reliability of a corroding concrete box girder for which half-cell potential measurements are available. This is the second main outcome of this thesis.
Both approaches presented in this thesis enable an integral reliability analysis of inspected and monitored structures that accounts for system effects arising from (a) the correlation among deterioration states of different structural elements, (b) the interaction between element deterioration and system failure, and (c) the indirect information gained on the condition of all unobserved structural elements from inspecting or monitoring the condition of some structural elements. Thus, both approaches enable a systemwide risk-based optimization of operation and maintenance strategies for deteriorating structural systems.
The NRA approach can be implemented relatively easily with subset simulation, which is a sequential Monte Carlo method suitable for estimating rare event probabilities. Subset simulation is robust and considerably more efficient than crude Monte Carlo simulation. It is, however, still sampling-based and its efficiency is thus a function of the number of inspection and monitoring outcomes, as well as the value of the simulated event probabilities. The current implementation of the NRA approach performs separate subset simulation runs to estimate the reliability at different points in time. The efficiency of the NRA approach with subset simulation can be significantly improved by exploiting the fact that failure events in different years are nested. The lifetime reliability of deteriorating structural systems can thus be computed in reverse chronological order in a single subset simulation run.
The implementation of the DBN approach is much more demanding than the implementation of the NRA approach but it has two main advantages. Firstly, the graphical format of the DBN facilitates the presentation of the model and the underlying assumptions to stakeholders who are not experts in reliability analysis. Secondly, it can be combined with exact inference algorithms. In this case, its efficiency neither depends on the number of inspection and monitoring outcomes, nor on the value of the event probabilities to be calculated. However, in contrast to the NRA approach with subset simulation, the DBN approach with exact inference imposes restrictions on the number of random variables and the dependence structure that can be implemented in the model.
Kontinuierliche sensorbasierte Bauwerksmessungen leisten einen wichtigen Beitrag zur Sicherheit von Verkehrsbauwerken. Hierzu werden im Vorhaben AISTEC Referenzbauwerke und Referenzverfahren untersucht mit Schwerpunkt auf den Einfluss klimatischer Bedingungen. Der Vortrag stellt den aktuellen Projektstand des FB 7.2 vor.
Dieser Bericht beschreibt ein Systemmodell für eine integrale Ermittlung und Prognose der Schadens- und Zustandsentwicklung der Elemente eines Brückensystems unter Berücksichtigung von Ergebnissen aus Inspektionen und Überwachung. Das Systemmodell wurde anhand eines ausgesuchten Spannbetonüberbaus in einzelliger Kastenbauweise entwickelt. Es besteht aus zwei integralen Teilmodellen: ein Modell zur Beschreibung des Systemschädigungszustandes und ein Modell zur Beschreibung der Standsicherheit. Für die Modellierung des stochastischen Systemschädigungszustandes eines Brückensystems werden dynamische Bayes'sche Netze (DBN) vorgeschlagen. Dieser Ansatz ermöglicht es, alle relevanten Schädigungsprozesse und deren stochastische Abhängigkeiten zu berücksichtigen. Ein wesentlicher Vorteil dieses Ansatzes ist es, dass DBN ideal dafür geeignet sind, Bayes'sche Aktualisierungen auf Grundlage von Informationen aus Inspektionen und Überwachungsmaßnahme auf eine effiziente und robuste Art und Weise durchzuführen. Der DBN-Ansatz ist deshalb für die Entwicklung von Software für das Erhaltungsmanagement von alternden Brückenbauwerken, die vom Benutzer keine vertieften Kenntnisse der Zuverlässigkeitstheorie verlangt, ideal geeignet. Für die Modellierung der Standsicherheit eines alternden Kastenträgers wird vereinfachend Biegeversagen des globalen Längssystems betrachtet. Zur Berechnung der maximalen Traglast eines Kastenträgers infolge des Systemschädigungszustandes wird ein plastisch-plastisches Verfahren eingesetzt, wobei die Beanspruchungen mittels der Fließgelenktheorie unter Ausnutzung der plastischen Beanspruchbarkeit der Querschnitte des Kastenträgers ermittelt werden. Ein Kastenträger versagt, wenn sich durch die Ausbildung einer ausreichend großen Anzahl von Fließgelenken eine kinematische Kette ausbildet. Dieser Modellierungsansatz berücksichtigt Redundanzen, die sich aus der plastischen Beanspruchbarkeit der Querschnitte und der statischen Unbestimmtheit eines Kastenträgers ergeben. Zum Nachweis der praktischen Einsetzbarkeit des entwickelten Systemmodells wurde ein Software-Prototyp entwickelt, der eine intuitiv benutzbare graphische Benutzeroberfläche (Front-End) mit einem Berechnungskern (Back-End) koppelt. Die aktuelle Version des Software-Prototyps implementiert ein Modell der chloridinduzierten Bewehrungskorrosion und ein Tragwerksmodell, welches das Verfahrens der stetigen Laststeigerung zur Bestimmung der maximalen Traglast des Kastenträgers auf der Grundlage eines Finite-Elemente-Modells umsetzt. Zur Durchführung von Bayes'schen Aktualisierungen des Systemschädigungszustandes auf der Grundlage des DBN-Modells implementiert der Prototyp den Likelihood-Weighting-Algorithmus. Die entwickelte Architektur des Prototyps ermöglicht eine Erweiterung der Software um weitere Schädigungsprozesse. Der entwickelte Software-Prototyp ermöglicht Benutzern ohne vertiefte Kenntnisse der Zuverlässigkeitstheorie eine Berechnung des Einflusses von Bauwerksinformationen auf den Systemschädigungszustand und die Tragsicherheit eines Kastenträgers. Auf dieser Grundlage können effiziente Inspektions- und Überwachungsmaßnahmen identifiziert und das Erhaltungsmanagement optimiert werden.
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.
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.
Reliability analysis of deteriorating structural systems requires the solution of time-variant reliability problems.
In the general case, both the capacity of and the loads on the structure vary with time. This analysis can be approached by approximation through a series of time-invariant reliability problems, which is a potentially effective strategy for cases where direct solutions of the time-variant reliability problem are challenging, e.g. for structural systems with many elements or arbitrary load processes. In this contribution, we thoroughly Review the formulation of the equivalent time-invariant reliability problems and extend this approximation to structures for which inspection and monitoring data is available. Thereafter, we present methods for efficiently evaluating the reliability over time. In particular, we propose the combination of sampling-based methods with a FORM (first-order reliability method) approximation of the series system reliability problem that arises in the computation of the lifetime reliability. The framework and algorithms are demonstrated on a set of numerical examples, which include the computation of the reliability conditional on inspection data.
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.
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.
Identifying optimal inspection and repair strategies for offshore jacket structures is a challenging task. We pre-sent an approach, which is based on recent developments in the field of risk-based operation and maintenance planning at the structural system level. The approach utilizes heuristics to define inspection and repair strate-gies at the system level and to reduce the search space of possible strategies. For each defined strategy, the expected service life cost of inspection, repair and failure is evaluated based on simulated inspection and re-pair histories. Subset simulation is applied to compute the conditional repair and failure probabilities required for this analysis. It also forms the basis for simulating inspection and repair histories. The strategy that mini-mizes the expected service life cost is the optimal one in the set of pre-selected strategies. The underlying condition and performance model accounts for the stochastic dependence among the deterioration states of the different structural elements and the structural redundancy. The approach is demonstrated in a case study considering a jacket-type frame. In this study, we essentially vary the inspection interval, the minimum num-ber of inspected components and the target reliability, and identify the combination that minimizes the ex-pected total service life cost.
Identifying optimal inspection and repair strategies for offshore jacket structures is a challenging task. We pre-sent an approach, which is based on recent developments in the field of risk-based operation and maintenance planning at the structural system level. The approach utilizes heuristics to define inspection and repair strate-gies at the system level and to reduce the search space of possible strategies. For each defined strategy, the expected service life cost of inspection, repair and failure is evaluated based on simulated inspection and re-pair histories. Subset simulation is applied to compute the conditional repair and failure probabilities required for this analysis. It also forms the basis for simulating inspection and repair histories. The strategy that mini-mizes the expected service life cost is the optimal one in the set of pre-selected strategies. The underlying condition and performance model accounts for the stochastic dependence among the deterioration states of the different structural elements and the structural redundancy. The approach is demonstrated in a case study considering a jacket-type frame. In this study, we essentially vary the inspection interval, the minimum num-ber of inspected components and the target reliability, and identify the combination that minimizes the ex-pected total service life cost.
Die Risikoanalyse ist ein formales Verfahren zur Unterstützung von Entscheidungen unter Unsicherheit und Risiko. Es ermöglicht eine Optimierung von Maßnahmen zur Verhinderung unerwünschter Konsequenzen. Der Vortrag stellt die aktuellen Aktivitäten an der BAM in dem Feld Risikoanalyse technischer System vor.
A novel method for risk-based optimization of inspection and repair strategies for deteriorating structural systems has recently been proposed. The method defines heuristics at the system level to reduce the number of possible strategies. For each defined strategy, it computes the updated system failure probability conditional on simulated inspection and repair histories, and evaluates the associated costs and risk. The expected total service life costs and risk for a strategy are finally determined using Monte Carlo simulation. The optimal strategy minimizes the expected total service life costs and risk. We intend to adopt this approach to optimize inspection, monitoring and repair activities for offshore wind park support structures. As a first step, we simulate – in analogy to an offshore wind park – the service life performance of an inspected group of jacket-type frames. The performance is quantified in terms of the group’s system failure probability conditional on simulated inspection and repair histories. The underlying system model accounts for the structural redundancy of the frames and the interdependence among their failure events due to similar loading conditions. The model also captures stochastic dependence among the deterioration states of the frames. As part of the simulation process the a-priori unknown outcome of any planned inspection is generated conditional on the outcome of all previous inspections.
A novel method for risk-based optimization of inspection and repair strategies for deteriorating structural systems has recently been proposed. The method defines heuristics at the system level to reduce the number of possible strategies. For each defined strategy, it computes the updated system failure probability conditional on simulated inspection and repair histories, and evaluates the associated costs and risk. The expected total service life costs and risk for a strategy are finally determined using Monte Carlo simulation. The optimal strategy minimizes the expected total service life costs and risk. We intend to adopt this approach to optimize inspection, monitoring and repair activities for offshore wind park support structures. As a first step, we simulate – in analogy to an offshore wind park – the service life performance of an inspected group of jacket-type frames. The performance is quantified in terms of the group’s system failure probability conditional on simulated inspection and repair histories. The underlying system model accounts for the structural redundancy of the frames and the interdependence among their failure events due to similar loading conditions. The model also captures stochastic dependence among the deterioration states of the frames. As part of the simulation process the a-priori unknown outcome of any planned inspection is generated conditional on the outcome of all previous inspections.
Das Ermüdungsverhalten einzelner Schweißverbindungen in Offshore-Strukturen ist stark korreliert. Diese Korrelation entsteht zum einen durch die Verwendung gleicher Prozesse und Materialien bei der Herstellung der Verbindungen, zum anderen sind die ermüdungswirksamen Lasten an verschiedene Hospots im Tragwerk ebenfalls von einander abhängig. Hieraus ergeben sich Systemeffekte, die es ermöglichen, durch stichprobenartige Inspektion des Zustandes einzelner Hotspots auf den Zustand der verbleibenden Hotspots im Tragwerk zu schließen. Auf dieser Grundlage können Inspektions- und Reparaturstrategien für ermüdungsbeanspruchte Offshore-Strukturen mit Hilfe von riskobasierten Methoden optimiert werden. In diesem Vortrag wird weiterführend ein Konzept zur Einbindung von globalen Monitoringergebnissen in die risikobasierte Inspektionsplanung vorgestellt.
Brücken müssen laufend überwacht werden, damit die Unsicherheiten hinsichtlich ihres Zustands, ihrer Beanspruchung und ihrer Leistungsfähigkeit verringert werden können. Diese Aufgabe soll künftig mit digitalen Methoden erleichtert werden. Im folgenden Beitrag werden deshalb die digitale Bauwerksmodellierung und die Entscheidungsfindung beleuchtet. Dazu wird gezeigt, wie Zustandsdiagnosen und -prognosen digital ermöglicht werden und wie durch diagnostische und prognostische Modelle eine wissenschaftliche Basis für risikobasierte Entscheidungen über Erhaltungsmaßnahmen und für den Übergang vom reaktiven zum vorausschauenden Brückenmanagement gebildet werden kann. Dabei wird klar: Jede Brücken-Überwachung bedarf einer Gesamtmethodik, ihre wichtigsten Elemente sind: Datenerfassung, Datenmanagement, Datenanalyse, Bauwerksmodellierung, Bauwerksbewertung und die letztendlichen Entscheidungen über notwendige Erhaltungsmaßnahmen.
Information on the condition and reliability of an offshore jacket structure provided by a vibration-based structural health monitoring system can guide decisions on inspection and maintenance. When selecting the sensor setup, the designer of the monitoring system must assess its overall benefit compared to its costs before installation. The potential benefit of continuously monitoring the dynamic response of a jacket structure can be formally quantified through a value of information analysis from Bayesian decision theory. In this contribution, we present a framework for optimizing the placement of vibration sensors on offshore jacket structures by maximizing the value of information of the monitoring system. To solve the resulting discrete optimization problem, we adapt a genetic algorithm. The framework is demonstrated in a numerical example considering a redundant jacket-type steel frame. The numerical study shows that monitoring the vibration response of the frame is beneficial. Good sensor setups consist of relatively few sensors located towards the upper part of the
frame. The adapted genetic algorithm performs similarly well as established sequential sensor placement algorithms and holds substantial promise for application to real jacket structures.
The Bundesanstalt für Materialforschung und -prüfung (BAM) collaborates with TNO to develop a software framework for automated calibration of structural models based on monitoring data. The ultimate goal is to include such models in the asset management process of engineering structures. As a basis for developing the framework, a multi-span road bridge consisting of ten simply supported steel box girders was selected as a test case. Our group measured output-only vibration data from one box girder under ambient conditions. From the data, we determined eigenfrequencies and mode shapes. In parallel, we developed a preliminary structural model of the box girder for the purpose of predicting its modal properties. In this contribution, we provide an overview of the measurement campaign, the operational modal analysis, the structural modeling and qualitatively compare the identified with the predicted modes. As an outlook, we discuss the further steps in the calibration process and future applications of the calibrated model.
The Bundesanstalt für Materialforschung und -prüfung (BAM) collaborates with TNO to develop a software framework for automated calibration of structural models based on monitoring data. The ultimate goal is to include such models in the asset management process of engineering structures. As a basis for developing the framework, a multi-span road bridge consisting of ten simply supported steel box girders was selected as a test case. Our group measured output-only vibration data from one box girder under ambient conditions. From the data, we determined eigenfrequencies and mode shapes. In parallel, we developed a preliminary structural model of the box girder for the purpose of predicting its modal properties. In this contribution, we provide an overview of the measurement campaign, the operational modal analysis, the structural modeling and qualitatively compare the identified with the predicted modes. As an outlook, we discuss the further steps in the calibration process and future applications of the calibrated model.
The levelized cost of energy (LCoE) is an important measure to quantify the macro-economic efficiency of an offshore wind farm and to enable a quantitative comparison with other types of energy production. The costs of the structural integrity management - which is required to ensure an adequate lifetime reliability of the turbine support structures - are part of the operational expenditures of an offshore wind farm. An optimization of the structural integrity management may reduce the operational expenditures and consequently the LCoE. However, the effect of the structural integrity management on the LCoE is hardly known. To investigate this effect, this paper presents a sensitivity analysis of the LCoE of a generic offshore wind farm. The probabilistic models of the parameters influencing the LCoE are based on a literature study including an explicit model for the structural integrity management.
The analysis reveals that LCoE may potentially be reduced if an optimization of the structural integrity management enables a service life extension.
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.
Der zukünftige Eurocode 7 wird ausdrücklich die Nutzung zuverlässigkeitsbasierter Methoden in der geotechnischen Planung und Bemessung erlauben. In Deutschland gibt es bisher kaum Erfahrung in der praktischen Anwendung derartiger Verfahren und entsprechend sind die Vorbehalte gegenüber diesen Methoden oft groß. Der neue DGGT-Arbeitskreis (AK) 2.15 „Zuverlässigkeitsbasierte Methoden in der Geotechnik“ hat sich zum Ziel gesetzt, durch praxisorientierte Anleitungen und Empfehlungen sowie begleitende Aus- und Weiterbildungsangebote den praktischen Zugang zu diesen Verfahren zu unterstützen. Ziel ist es, Möglichkeiten und Grenzen zu verdeutlichen sowie vor allem ihre Potenziale zu erschließen. In dem vorliegenden Beitrag werden allgemeine Grundlagen und die zukünftigen Arbeitsfelder des AK 2.15 vorgestellt. Ausgehend von der Einführung relevanter Fachbegriffe wird zunächst die Einbettung zuverlässigkeitsbasierter Verfahren in den aktuellen Normungs- und Regelungskontext aufgezeigt. Anschließend werden anhand des Lebenszyklus eines geotechnischen Bauwerks die Unsicherheiten in den geotechnischen Prognosen und Bewertungen beschrieben. Daran anknüpfend wird aufgezeigt, an welchen Stellen zuverlässigkeitsbasierte Methoden als mögliches Werkzeug sinnvoll genutzt werden können, um Ingenieur:innen, Bauherr:innen und Prüfer:innen in Nachweis- und Entscheidungsprozessen zu unterstützen. Zu den sich daraus ableitenden Arbeitsthemen werden durch den AK 2.15 zukünftig Empfehlungen erarbeitet und sukzessive veröffentlicht