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Structural Health Monitoring (SHM) systems are designed for assisting owners and operators with information and forecasts concerning the fitness for purpose of structures and building systems. The benefit associated with the implementation of SHM may in some cases be intuitively anticipated or proven by past experiences but in general there appears to be no rational or systematic approach for assessing the value of SHM systems a-priory to their implementation. The present paper addresses the assessment of the value of SHM with basis in structural risk assessments and the Bayesian pre-posterior decision analysis. The quantification of the value of SHM builds upon the quantification of the value of information (VoI) or rather the benefit of monitoring. The suggested approach involves a probabilistic representation of the loads and environmental conditions acting on structures as well as their responses and performances over their life-cycle. In addition, the quality of monitoring and the performance of possible remedial actions triggered by monitoring results are modeled probabilistically.The consequences accounted for, in principle include all consequences associated with the performance of the structure over its life-cycle as well as the costs associated with monitoring and possible remedial actions. The suggested approach is illustrated through two case studies concerning the monitoring of welded details in steel structures subjected to fatigue loading. The case studies address the effect of the uncertainty associated with the performance of SHM on the value of SHM. Moreover, in order to illustrate the potential of the application of approach for monitoring of structural systems an optimal strategy for SHM is determined for a system comprised of three welded details.
Das Bauwerk einer Windenergieanlage dient der Sicherstellung der Energieproduktion. Eine entsprechende Zuverlässigkeit der Struktur und deren permanente Bewertung und Unterhaltung ist dazu notwendig. In diesem Beitrag werden Möglichkeiten der Bewertung des für die Struktur wichtigen Schädigungsmechanismus Ermüdung sowie dessen Überwachung in Zusammenhang mit Schädigungsindikatoren aufgezeigt. Dabei wird auf strukturmechanische, werkstoffmechanische und probabilistische Modelle sowie auf Messdaten eines Prototyps einer OWEA zurückgegriffen. Ausgehend von der gesamtdynamischen Berechnung wird die Zuverlässigkeit gemäß der Auslegung für den Schädigungsmechanismus Ermüdung durch ein Wöhlerlinienmodell berechnet. Damit werden ermüdungssensible Komponenten der Struktur identifiziert. Schädigungsindikatoren zur Überwachung der Ermüdungsfestigkeit werden auf Grundlage schädigungs-äquivalenter Dehnungen vorgestellt.
Fatigue assessment of support structures for offshore wind energy converters.
The structure of wind energy converters secures a reliable energy production. Hence an adequate reliability of the structure and additionally permanent assessment and maintenance is required. Within this paper methods for the assessment of the important damage mechanism fatigue in combination with monitoring of the fatigue reliability utilizing damage indicators are proposed. These methods comprise structural system, material degradation and probabilistic models as well as monitoring data of an offshore wind energy converter prototype. The structural reliability of the structure as designed is calculated based on an overall dynamic analysis using a SN-approach. Herewith fatigue sensitive components are identified. Damage indicators for monitoring the fatigue reliability based on damage equivalent strains are presented.
As more offshore wind parks are commissioned, the focus will inevitably shift from a planning, construction, and warranty focus to an operation, maintenance, and investment payback focus. In this latter case, both short-term risks associated with wind turbine component assemblies, and long-term risks related to integrity of the support structure, are highly important. This research focuses on the role of condition monitoring to lower costs and risks associated with short-term reliability and long-term asset integrity. This enables comparative estimates of the life cycle costs and reduction in uncertainty, both of which are of value to investors.
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.
Reclassification of bridges, i.e., a change in load rating, using reliability-based methods and a direct update with proof load information has been presented by many authors. However, bridge reclassification has hardly been studied from a decision analytic perspective, i.e., with quantification of the risks and benefits of different classification choices, and the expected benefit gain from proof loading. We derive, explain and exemplify a decision analytic approach for bridge reclassification along with models for (1) elastic and ultimate capacity and their adaptation with proof load information, (2) proof load information with classification outcomes accounting for target reliabilities and, (3) utilities including socio-economic benefits from reclassification. The approach and models are exemplified with a case study based on reclassification of bridges with a low existing classification. Decision rules, for practical use by a highway authority to find the optimal classification, are identified and documented based on: (1) the measurement of the capacity at elastic limit by proof loading, (2) the bridge reclassification benefits, and, (3) the required annual reliability level. From a Value of Information analysis, it is concluded that the proof load information is highly valuable for reclassification in cases of high socio-economic benefits and high reliability requirements.
Proof load testing may be performed to confirm the reliability of the bridge for an existing classification or to prove the reliability for a higher classification. In this paper, a probabilistic decision analysis approach is applied to the scenario for the evaluation of target proof load in the situation where information on the bridge resistance model is lacking. In this case, the resistance model is established by proof loading and taking very basic prior knowledge into account. The decision scenario is modelled in the context of the proof load test planner who shall choose the required load level for assessment of a bridge. The choice of the load level depends on the risks due to the testing and the expected benefit gain from the test. Information acquired about the loading response from monitoring during the proof load testing is modelled by taking basis in the model uncertainty formulation. The optimal proof load level for classification of a single lane, simply supported bridge of 8m span subjected to live load from very heavy (gross weight > 80 tons) transport vehicles was calculated. The optimal proof load level was identified as leading to a positive expected benefit gain to the decision maker while also satisfying target reliability criteria for remaining service life. The analysis was performed for the evaluation of bridge performance with respect to five classifications of very heavy transport vehicles with different vehicle weights and configurations.
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.
A method to determine the influencing parameters of a structural and damage detection system is proposed based on the value of Information analysis. The value of information analysis utilizes the Bayesian pre-posterior decision theory to quantify the value of damage detection system for the structural integrity management during service life. First, the influencing parameters of the structural system, such as deterioration type and rate are introduced for the performance of the prior probabilistic system model. Then the influencing parameters on the damage detection system performance, including number of sensors, sensor locations, measurement noise, and the Type-I error are investigated. The preposterior probabilistic model is computed utilizing the Bayes’ theorem to update the prior system model with the damage indication information. Finally, the value of damage detection system is quantified as the difference between the maximum utility obtained in pre-posterior and prior analysis based on the decision tree analysis, comprising structural probabilistic models, consequences, as well as benefit and costs analysis associated with and without monitoring. With the developed approach, a case study on a statically determinate Pratt truss bridge girder is carried out to validate the method. The analysis shows that the deterioration rate is the most sensitive parameter on the effect of relative value of information over the whole service life. Furthermore, it shows that more sensors do not necessarily lead to a higher relative value of information; only specific sensor locations near the highest utilized components lead to a high relative value of information; measurement noise and the Type-I error should be controlled and be as small as possible. An optimal sensor employment with highest relative value of information is found. Moreover, it is found that the proposed method can be a powerful tool to develop optimal service life maintenance strategies—before implementation—for similar bridges and to optimize the damage detection system settings and sensor configuration for minimum expected Costs and risks.
Inspections are an efficient means of enhancing the reliability of redundant structural Systems subjected to fatigue. To investigate the effect of such inspections, we represent the deterioration state of a Daniels System by means of a probabilistic fatigue crack growth model of all elements, which considers stochastic dependence among element fatigue behavior. We include inspection results in the calculation of the System collapse probability through Bayesian updating of the System deterioration state. Based on this approach, we calculate the collapse probability of a deteriorating Daniels System conditional on different inspection strategies in terms of inspection coverage and inspection times. The acceptability of an inspection strategy is verified by comparing the calculated collapse probabilities with maximum acceptable System failure probabilities. This study is a Step towards identifying optimal inspection strategies for redundant structural Systems subjected to fatigue.
Fatigue and serviceability limit state model basis for assessment of offshore wind energy converters
(2012)
This paper develops the models for the structural performance of the loading and probabilistic characterization for the fatigue and the serviceability limit states for the support structure of offshore wind energy converters. These models and a sensitivity study are part of a risk based assessment and monitoring framework and will be applied for establishing the 'as designed and constructed' reliability as prior information for the assessment and the design of monitoring systems. The constitutive physical equations are introduced in combination with the fatigue and serviceability limit state requirements as the starting point for the development of the structural performance and loading models. With these models introduced in detail, several modeling aspects for both limit states are analyzed. This includes analyses of the influence on the hot spot stresses by applying a contact formulation for the pile guide brace connection and the application of a finite element formulation using solid elements. Further, the comparison of the natural frequencies of a discrete rotor model with a continuous rotor model is documented. To account for uncertainties associated with the structural and loading models, a probabilistic model is derived on the basis of literature review and measurement data from a prototype Multibrid M5000 support structure. The sensitivity study is based on the calculation of a nonlinear coefficient of correlation in conjunction with predetermined designs of experiments. This is conducted by a systematic analysis of the influence of the random variables on limit state responses and hence on the structural reliability. Integrating the analyses and sensitivity studies of the fatigue and serviceability limit state models developed in this paper as well as the ultimate limit state models in Thöns et al. ('Ultimate Limit State Model Basis for Assessment of Offshore Wind Energy Converters,' ASME J. Offshore Mech. Arct. Eng.), the model basis for the assessment is completed. The process of establishing and analyzing such a model basis contributes to a detailed understanding of the deterministic and probabilistic characteristics of the structure and provides valuable insights in regard to the significance of available data.
In the present paper, an approach for updating the continuous stress range distribution of a welded connection of a wind turbine support structure with predicted information from strain measurements is presented. Environmental conditions, such as wind or, in offshore fields, waves and currents, in combination with rotor excitations generate cyclic stresses affecting the reliability of welded joints of the support structure over the service life. Using strain measurements, these conditions can be monitored, and the resulting stress ranges, under consideration of measurement, mechanical and material uncertainties, can be reconstructed. These stress ranges can be used as an input for updating the prior probability density function (PDF) of the stress ranges predicted by the overall dynamics and a detailed design analysis. Applying Bayesian probability theory and decision theoretical implications, the predicted posterior probability density of the stress ranges is calculated based on the design information and uncertainties. This approach is exemplified, and it is shown how the predicted stress ranges and the design stress ranges are distributed. The prior and the predicted posterior stress ranges are used for a reliability calculation for potentially entering a pre-posterior decision 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.
The use of load and structural performance measurement information is vital for efficient structural integrity management and for the cost of energy production with Offshore Wind Turbines (OWTs). OWTs are dynamically sensitive structures subject to an interaction with a control unit exposed to repeated cyclic wind and wave loads causing deterioration and fatigue. This study focuses on the quantification of the value of structural and environmental information on the integrity management of OWT structures, with the focus on fatigue of welded joints. By utilizing decision analysis, structural reliability methods, measurement data, as well as the cost-benefit models, a Value of Information (VoI) analysis can be performed to quantify the most beneficial measurement strategy. The VoI assessment is demonstrated for the integrity management of a butt welded joint of a monopile support structure for a 3 MW OWT with a hub height of approximately 71m. The conditional value of three-year measured oceanographic information and one-year strain monitoring information is quantified posteriori in conjunction with an inspection and repair planning. This paper provides insights on how much benefits can be achieved through structural and environmental information, with practical relevance on reliability-based maintenance of OWT structures.
Wind turbines on offshore sites (OWECs) are subjected to combined loads from wind and waves. These dynamic loads, with a frequency content within the range of the natural frequencies of the structures, cause fatigue-effective stresses in the substructures of wind turbines. Therefore, the examination of natural frequencies is an important part within the design process of wind turbines. The quality of the numerical models for such calculations is of great importance, since the certification guidelines permit only small uncertainties in modal analysis results. The accuracy of the parameters of the numerical model can only be achieved through a comparison of simulation results with corresponding test results. Therefore, it is necessary to measure the dynamic behaviour of all components of the wind turbines simultaneously. This is true not only for the design verification, but also for monitoring the OWECs in operation. The potential of integrated systems for monitoring-based maintenance optimisation should thus be used.
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.
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.
Damage detection systems (DDS) provide information of the structural system integrity in contrast to e.g. local information by inspections or non-destructive testing techniques. In this paper, an approach is developed and demonstrated to utilize DDS information to update the structural system reliability and to integrate this information in structural system risk and utility analyses. For this aim, a novel performance modelling of DDS building upon their system characteristics and non-destructive testing reliability is introduced. The DDS performance modelling accounts for a measurement system in combination with a damage detection algorithm attached to a structural system in the reference and damage states and is modelled with the probability of indication accounting for type I and II errors. In this way, the basis for DDS performance comparison and assessment is provided accounting for the dependencies between the damage states in a structure. For updating of the structural system reliability, an approach is developed based on Bayesian updating facilitating the use of DDS information on structural system level and thus for a structural system risk analysis. The structural system risk analysis encompasses the static, dynamic, deterioration, reliability and consequence models, which provide the basis for the system model for calculating the direct risks due to component failure and the indirect risks due to system failure. Two case studies with the developed approach demonstrate a high Value of DDS Information due to risk and expected cost reduction.
We analyse the performance of risk reduction strategies for Terrorist attacks with Improvised Explosive Devices (IEDs) for large governmental building structures in terms of cost-efficiency, significance and effectiveness Accounting for life safety in conjunction with societal preferences and capabilities. The approach builds upon an extended Bayesian pre-posterior decision analysis and the principles of the marginal lifesaving costs based on the Life Quality Index (LQI). The decision scenario is formulated for a decision maker responsible for the safety of governmental or large commercial buildings and consequently the direct risks, the indirect risks due to fatalities and economical importance of the building beside the expected cost for the individual risk reduction strategies are modelled, aggregated and optimised. The considered risk reduction strategies encompass an explicit consideration and distinction of information and actions such as (i) threat surveillance may trigger the temporary evacuation of the building, (ii) the implementation of protection provisions provided by codes and guidelines, (iii) a detailed progressive collapse assessment and specific protection measures and (iv) the combination of protection and surveillance. All considered strategies are found to contribute to risk reduction and can be costefficient, especially for higher threat probabilities. The risk reduction strategies comply with societal macroeconomic and demographical characteristics and societal preferences according to the LQI. The progressive collapse assessment with targeted protection measures is found to be the most cost-efficient, significant and effective counter-terrorism strategy. This finding points to the necessity for a comprehensive utilisation of scientific methods and sophisticated engineering for progressive collapse assessment to determine targeted protection measures.
This paper describes a structural reliability analysis utilizing monitoring data in the ultimate limit state with consideration of the uncertainties of the monitoring procedure. For this purpose the uncertainties of the monitoring data are modeled utilizing a new framework for the determination of measurement uncertainties. The approach is based on a process equation and Statistical models of observations for the derivation of a posterior measurement uncertainty by Bayesian updating. This facilitates the quantification of a measurement uncertainty using all available data of the measurement process. For the reliability analysis in the ultimate limit state, monitoring data can be utilized as a loading model Information and as proof loading, i.e. resistance model Information. Both approaches are discussed with generic examples and it is shown that the modeling of monitoring data in a reliability analysis can result in a reduction of uncertainties and as a consequence in the reduction of the probability of failure. Furthermore, the proof loading concept is developed further to account for the uncertain characteristic of proof loading due to the measurement uncertainties which is consistent with the framework for the determination of measurement uncertainties. These approaches and findings can be utilized for the assessment of structures for life cycle extension and the design of monitoring Systems.
This article introduces an approach and framework for the quantification of the value of structural health monitoring (SHM) in the context of the structural risk and integrity management for systems. The quantification of the value of SHM builds upon the Bayesian decision and utility theory, which facilitates the assessment of the value of information associated with SHM. The principal approach for the quantification of the value of SHM is formulated by modeling the fundamental decision of performing SHM or not in conjunction with their expected utilities. The expected utilities are calculated accounting for the probabilistic performance of a system in conjunction with the associated structural integrity and risk management actions throughout the life cycle, the associated benefits, structural risks, and costs and when performing SHM, the SHM information, their probabilistic outcomes, and costs. The calculation of the expected utilities necessitates a comprehensive and rigorous modeling, which is introduced close to the original formulations and for which analysis characteristics and simplifications are described and derived. The framework provides the basis for the optimization of the structural risk and integrity management based on utility gains including or excluding SHM and inspection information. Studies of fatigue deteriorating structural Systems and their characteristics (1) provide decision Support for the performance of SHM, (2) explicate the influence of the structural component and system characteristics on the value of SHM, and (3) demonstrate how an integral optimization of SHM and inspection strategies for an efficient structural risk and integrity management can be performed.
Dieser Artikel enthält aktuelle Forschungsergebnisse im Bereich des überwachungsgestützten Managements der Tragwerksintegrität. Das Management der Tragwerksintegrität hat zum Ziel, die Funktionalität eines Tragwerks während des gesamten Lebenszyklus durch Einhaltung der Zielzuverlässigkeiten sicherzustellen. Ausgehend von Forschungsergebnissen über die Charakteristik von Überwachungsverfahren und -daten im Rahmen von Zuverlässigkeitsanalysen, wird ein Ansatz vorgestellt, wie die erwarteten Kosten für das Management der Tragwerksintegrität durch Überwachungsverfahren optimiert werden können. Dazu wird eine Kosten-Nutzen-Analyse für den Lebenszyklus einer Gründungstruktur von Offshore-Windenergieanlagen durchgeführt. Im Rahmen der Bayes'schen Entscheidungstheorie werden durch eine Prä-posteriori-Entscheidungsanalyse die optimalen Entscheidungsparameter für den Entwurf eines Überwachungssystems bestimmt. Als Fallstudie wird ein Überwachungssystem für die Gründungsstruktur eines Multibrid-M5000-Prototyps konzipiert und die mögliche Reduktion der Risiken und erwarteten Betriebskosten quantifiziert. --------------------------------------------------------------------------------------------------------------------------------------------------------------------
This paper contains recent research results in the field of monitoring supported structural integrity management. The structural integrity management aims at ensuring the functioning of a structure throughout the life cycle by compliance with the target reliabilities. Building upon research results concerning the characteristics of monitoring techniques and data within the framework of reliability analyses, an approach is introduced for the optimisation of the expected structural integrity management costs by monitoring information. For this aim, a life cycle cost benefit analysis for offshore wind turbine support structures is formulated. The optimal monitoring decision parameters are then assessed utilizing the Bayesian pre-posterior decision theory. As a case study a monitoring system for a Multibrid M5000 offshore wind turbine prototype support structure is designed and possible reductions of the risks and expected costs are quantified.
Predictive information and maintenance optimization for deteriorating structures is concerned with scheduling (a) the collection of information by inspection and monitoring and (b) maintenance actions such as repair, replacement, and retrofitting based on updated predictions of the future condition of the structural system. In this article, we consider the problem of jointly identifying—at the beginning of the service life—the optimal inspection time and repair strategy for a generic welded joint in a generic offshore wind turbine structure subject to fatigue. The optimization is performed based on different types of decision analyses including value of information analyses to quantify the expected service life cost encompassing inspection, repair, and fatigue damage for all relevant combinations of inspection time, repair method, and repair time. Based on the analysis of the expected service life cost, the optimal inspection time, repair method, and repair time are identified. Possible repair methods for a welded joint in an offshore environment include welding and grinding, for which detailed models are formulated and utilized to update the joint’s fatigue performance. The decision analyses reveal that an inspection should be scheduled approximately at mid-service life of the welded joint. A repair should be performed in the same year after an indication and measurement of a fatigue crack given an optimal inspection scheduling. This article concludes with a discussion on the results obtained from the decision and value of information analyses.
The fatigue deterioration modeling for welded steel structures subjected to high cycle fatigue is presented in the current document. The aim of this paper is to formulate approaches based on actual Research results for the phases fatigue crack initiation, fatigue propagation and fatigue failure. The physical characteristics of the approaches are discussed in combination with the associated uncertainties and the probabilistic modeling of this time variant reliability problem. The fatigue deterioration modeling documented here serve as a basis for a reliability-based approach to inspection planning.