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Eingeladener Vortrag
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Fatigue safety verification of existing bridges that uses ‘‘re-calculation’’ based on codes, usually results in insufficient fatigue safety, triggering invasive interventions. Instead of “re-calculation”, Structural Health Monitoring (SHM) should be used for the assessment of the existing bridges. Monitoring systems provide data that can reduce uncertainties associated with the fatigue loading process and the structural resistance. The objective of this paper is to quantify the value of the SHM system implemented in a 60-years-old road viaduct to investigate its fatigue safety, through modeling of the fundamental decisions of performing monitoring in conjunction with its expected utility. The quantification of the conditional value of information is based on the decision tree analysis that considers the structural reliability, various decision scenarios as well as the cost-benefit assessments. This leads to a quantitative decision basis for the owner about how much time and money can be saved while the viaduct fulfills its function reliably and respects the safety requirements. The originality of this paper stands in the application of the value of information theory to an existing viaduct considering the fatigue failure of the system based on the monitoring data and the cost-benefit of monitoring method.
Fires in road tunnels constitute complex scenarios with interactions between the fire, tunnel users and safety measures. More and more methodologies for risk analysis quantify the consequences of these scenarios with complex models. Examples for complex models are the computational fluid dynamics model Fire Dynamics Simulator (FDS) and the microscopic evacuation model FDS+Evac. However, the high computational effort of complex models often limits the number of scenarios in practice. To balance this drawback, the scenarios are often simplified. Accordingly, there is a challenge to consider complex scenarios in risk analysis.
To face this challenge, we improved the metamodel used in the methodology for risk analysis presented on ISTSS 2016. In general, a metamodel quickly interpolates the consequences of few scenarios simulated with the complex models to a large number of arbitrary scenarios used in risk analysis. Now, our metamodel consists of the projection array-based design, the moving least squares method, and the prediction interval to quantify the metamodel uncertainty. Additionally, we adapted the projection array-based design in two ways: the focus of the sequential refinement on regions with high metamodel uncertainties; and the combination of two experimental designs for FDS and FDS+Evac.
To scrutinise the metamodel, we analysed the effects of three sequential refinement steps on the metamodel itself and on the results of risk analysis. We observed convergence in both after the second step (ten scenarios in FDS, 192 scenarios in FDS+Evac). In comparison to ISTSS 2016, we then ran 20 scenarios in FDS and 800 scenarios in FDS+Evac. Thus, we reduced the number of scenarios remarkably with the improved metamodel. In conclusion, we can now efficiently integrate complex scenarios in risk analysis. We further emphasise that the metamodel is broadly applicable on various experimental or modelling issues in fire safety engineering.
Safety measures like tunnel emergency Ventilation Systems cause high financial costs. Hence, safety measures have to be chosen with the focus on the expected reduction of the consequences like fatalities or damage on structures and in conjunction with the investments. Since 2004, the European directive EU 2004/54/EC proposes therefore the application of risk assessments. Because the EU directive provides only few legal requirements on risk assessments, the methodologies developed on this basis have large differences. After one decade of intensive research, the comparative study now highlights common aspects and differences of several methodologies.
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.
Structural health monitoring of wind turbine blades by strain measurement and vibration analysis
(2011)
Wind turbine blades have to withstand a high number of load cycles in mostly hard weather conditions over 20 years. In a research project BAM and several partners have designed, evaluated and tested a condition monitoring system for all parts of a wind turbine. At a rotorblade with a length of 58 m fibre bragg grating sensors were applied for in-service strain measurement. Additionally a complex test rig was designed to enable real biaxial loading conditions at a representative test rotor blade to simulate the mechanical loading conditions in the lab. In this test blade with a length of 8 m also fibre bragg grating sensors were implemented to determine their influences for the structure and the condition monitoring system. Vibration measurements were carried out at different test phases. The results were compared with finite element strain and modal analysis.
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.
Support structures of offshore wind turbines are subject to cyclic stresses generated by different time-variant random loadings such as wind, waves, and currents in combinationwith the excitation by the rotor. In the design phase, the cyclic demand on wind turbine support structure is calculated and forecasted with semi or fully probabilistic engineering models. In some cases, additional cyclic stresses may be induced by construction deviations, unbalanced rotor masses and structural dynamic phenomena such as, for example, the Sommerfeld effect. Both, the significant uncertainties in the design and a validation of absence of unforeseen adverse dynamic phenomena necessitate the employment of measurement Systems on the support structures. The quality of the measurements of the cyclic demand on the support structures depends on (a) the precision of the measurement System consisting of sensors, amplifier and data normalization and (b) algorithms for analyzing and converting data to structural health information. This paper presents the probabilistic modelling and analysis of uncertainties in strain measurements performed for the purposes of reconstructing stress resultants in wind turbine towers.
It is shown how the uncertainties in the strain measurements affect the uncertainty in the individual components of the reconstructed forces and moments. The analysis identifies the components of the vector of stress resultants that can be reconstructed with sufficient precision.
Quantification of the posterior utilities of SHM campaigns on an orthotropic steel bridge deck
(2019)
This paper contains a quantification and decision theoretical optimization of the posterior utilities for several options for monitoring campaigns on the particular case of fatigue life predictions of an orthotropic steel deck. The monitoring campaigns are defined by varying monitoring durations and phases. The decision analysis is performed with real data from the Structural Health Monitoring (SHM) of the Great Belt Bridge (Denmark) which, among others, consist of measured strains, pavement temperatures and traffic intensities. The fatigue loading prediction model is based on regression models linking daily averaged pavement temperatures, daily aggregated heavy-traffic Counts and derived S-N fatigue damages, all of them derived from the outcomes of different monitoring campaigns. A probabilistic methodology is utilized to calculate the fatigue reliability profiles of selected instrumented welded joints. The posterior utilities of SHM campaigns are then quantified by considering the structural fatigue reliability, various monitoring campaigns and the corresponding cost-benefit models. The decisions of identifying the optimal monitoring campaign and of extending the service life or not in conjunction with monitoring results are modelled. The optimal monitoring campaign is identified - retrospectively - by maximizing the expected benefits and minimize risks in dependency of the monitoring duration and the monitoring associated costs. The results, despite relying on a number of simplistic assumptions, pave the way towards the use of pre-posterior decision support to optimise the design of monitoring campaigns for similar bridges, with an overall goal to proof the cost efficiency of SHM approaches to civil infrastructure management.
Quantification of the posterior utilities of SHM campaigns on an orthotropic steel bridge deck
(2019)
This paper contains a quantification and decision theoretical optimization of the posterior utilities for several options for monitoring campaigns on the particular case of fatigue life predictions of an orthotropic steel deck. The monitoring campaigns are defined by varying monitoring durations and phases. The decision analysis is performed with real data from the Structural Health Monitoring (SHM) of the Great Belt Bridge (Denmark) which, among others, consist of measured strains, pavement temperatures and traffic intensities. The fatigue loading prediction model is based on regression models linking daily averaged pavement temperatures, daily aggregated heavy-traffic Counts and derived S-N fatigue damages, all of them derived from the outcomes of different monitoring campaigns. A probabilistic methodology is utilized to calculate the fatigue reliability profiles of selected instrumented welded joints. The posterior utilities of SHM campaigns are then quantified by considering the structural fatigue reliability, various monitoring campaigns and the corresponding cost-benefit models. The decisions of identifying the optimal monitoring campaign and of extending the service life or not in conjunction with monitoring results are modelled. The optimal monitoring campaign is identified - retrospectively - by maximizing the expected benefits and minimize risks in dependency of the monitoring duration and the monitoring associated costs. The results, despite relying on a number of simplistic assumptions, pave the way towards the use of pre-posterior decision support to optimise the design of monitoring campaigns for similar bridges, with an overall goal to proof the cost efficiency of SHM approaches to civil infrastructure management.
This paper addresses the quantification of the value of damage detection system and algorithm information on the basis of Value of Information (VoI) analysis to enhance the benefit of damage detection information by providing the basis for its optimization before it is performed and implemented. The approach of the quantification the value of damage detection information builds upon the Bayesian decision theory facilitating the utilization of damage detection performance models, which describe the information and its precision on structural system level, facilitating actions to ensure the structural integrity and facilitating to describe the structural system performance and its functionality throughout the service life. The structural system performance is described with its functionality, its deterioration and its behavior under extreme loading. The structural system reliability given the damage detection information is determined utilizing Bayesian updating. The damage detection performance is described with the probability of indication for different component and system damage states taking into account type 1 and type 2 errors. The value of damage detection information is then calculated as the difference between the expected benefits and risks utilizing the damage detection information or not. With an application example of the developed approach based on a deteriorating Pratt truss system, the value of damage detection information is determined,demonstrating the potential of risk reduction and expected cost reduction.
This paper addresses how the value of damage detection Information depends on key Parameters of the Structural Health Monitoring (SHM) system including number of sensors and sensor locations. The Damage Detection System (DDS) provides the information by comparing ambient vibration measurements of a (healthy) reference state with measurements of the current structural system. The performance of DDS method depends on the physical measurement properties such as the number of sensors, sensor positions, measuring length and sensor type, measurement noise, ambient excitation and sampling frequency, as well as on the data processing algorithm including the chosen type I error for the indication threshold. The quantification of the value of Information (VoI) is an expected utility based Bayesian decision analysis method for quantifying the difference of the expected economic benefits with and without information. The (pre-)posterior probability is computed utilizing the Bayesian updating theorem for all possible indications. If changing any key parameters of DDS, the updated probability of system failure given damage detection information will be varied due to different indication of probability of damage, which will result in changes of value of damage detection information. The DDS system is applied in a statically determinate Pratt truss bridge girder. Through the analysis of the value of information with different SHM system characteristics, the settings of DDS can be optimized for minimum expected costs and risks before implementation.
Vehicle fires in tunnels can have catastrophic consequences for the road users, the property and traffic inffastructure. To support an evacuation planning, this study simulates the fire smoke toxicity and the smoke layer of a vehicle fire in a full-size test tunnel. The three dimensional prediction of the fire smoke toxicity in the test tunnel is realized by implementing the Fractional Effective Dose and the Fractional Summation concept in a CFD environment. The developed model facilitates to calculate fire scenarios for various types of tunnels and to quantify the hazard e.g. during an evacuation scenario.
To ensure a high operational reliability of future generations of offshore wind conversion systems (OWEC) with economically acceptable repair and maintenance efforts, comprehensive diagnosis and supervision concepts are required. Automatic monitoring systems will be an essential part of such concepts. Because of the fact, that during operation there will be static and dynamic interaction between the components 'structure', 'machinery' and 'blades' it is necessary to develop the monitoring techniques in an overall concept. These monitoring systems are supposed to be applied for the design and testing as well as for the operation and maintenance phases. The used methods are focused on the design requirements for the structure, which are stated in the respective standards and certification guidelines. For the validation of the design, methods are specified which would be also applicable for SHM in the operating condition, e.g. for the evaluation of the structural conditon possibly after the occurrence of damages or changes. The further task consists in the realization of condition monitoring for all components, aiming at early damage detection and the observation of the damage development and its evaluation. Here the task consists of determining with assessment procedures the optimum time for maintenance actions to be carried out.
To ensure a high operational reliability of offshore wind turbines (OWEC) with economically acceptable repair and maintenance efforts, comprehensive diagnosis and supervision concepts are required. Automatic monitoring Systems will be an essential part of such concepts. Because of the fact, that during Operation there will be static and dynamic interaction between the components ‘structure’, ‘machinery’ and ‘blades’ it is necessary to develop the monitoring techniques in an overall concept. These monitoring Systems are supposed to be applied for the design and testing as well as for the Operation and maintenance phases. The knowledge of the dynamic behavior of wind turbines is important both for the design and for a safe Operation. The available monitoring data from a period of three years, allow first conclusions on the long-term Operation of such Systems in terms of quality requirements to the instrumentation to the structure and the rotor blades
The concept and technical details of the implementation of the developed integrated monitoring system within the IMO-WIND project are presented. The tasks of the components of the system and its requirements are described. Selected results from the continuous monitoring during operation of the plant M5000_2 regarding the task design verification and dynamic structural analysis are given.
Prototypes of wind turbines of the megawatt dass are to be built and tested until 2008 within a German offshore wind energy test field in the North Sea (ALPHA VENTUS). To ensure a high operational reliability of offshore wind turbines with economically acceptable repair and maintenance efforts, comprehensive diagnosis and supervision concepts are required. Automatic monitoring Systems will be an essential part of such concepts. Because of the fact, that during Operation there will be static and dynamic interaction between the components ‘structure’, ‘machinery’ and ‘blades’ it is necessary to develop the monitoring techniques in an overall concept. These monitoring Systems are supposed to be applied for the design and testing as well as for the Operation and maintenance phases. In the paper the developed approaches for the measurement of actions and the condition monitoring of all components of an offshore wind energy plant will be shown.