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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.
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 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.
Vibration-based structural health monitoring of a wind turbine system. Part I: Resonance phenomenon
(2015)
This paper is focused on a resonance phenomenon of a wind turbine system in 5 MW class, on the basis of dynamic signals acquired continuously from the tubular tower under normal operational conditions during two years.
Firstly, technique specifications of the wind turbine system are introduced and a finite element model is developed to characterize the structural dynamic properties. The following part describes the continuous dynamic monitoring system integrated with an automated operational modal analysis procedure using the poly-reference Least Squares Complex Frequency domain (p-LSCF) method. Subsequently, variations and mutual relationships of environmental/operational factors such as vibration amplitude, temperature, wind speed, rotation speed of blades, pitch angle and nacelle direction are also presented. Finally, significant resonance is observed due to the fundamental frequency of the tower matching with the harmonic frequency induced by the rotation of three blades. As the rotation speed of rotor approaches to 8 rpm, the vibration amplitude of the tower increases significantly and the corresponding damping value decreases. With the further rising wind velocity, the rotation speed of blades stops increasing and the input energy just contribute to accumulate the vibration amplitude of tower. Such observation indicates the Sommerfeld effect that aggravates the resonance phenomenon. A vibration control device is necessary to minimize the excessive structural responses.
A companion paper will further discuss the environmental/operational effects on dynamic properties of the wind turbine system under the operational conditions.
The second part of these companion papers mainly researches environmental/operational influences on structural dynamic properties under normal operational conditions during two years, in order to extract a statistical based damage-sensitive indicator for health monitoring of a wind turbine system.
The correlation analyses between experimental identified frequencies, damping values as well as mode shapes and environmental/operational factors such as rotation speed of blades, wind speed, pitch angle, temperature and nacelle direction are presented. It is observed that the frequency estimates are influenced by the nacelle position, the activation of rotor, the rotation speed of blades and the wind speed as well as the temperature. Regarding to the damping estimates, they are mainly associated with variation of the aerodynamic damping due to the increasing wind speed. Besides, the resonance phenomenon is also observed in higher modes. The harmonic frequencies due to blades passing by tower are found and the corresponding damping value decreases. Moreover, the mode shapes in some modes are strongly affected by the position of the nacelle.
Subsequently, two types of simulated damage including the reduction of stiffness in both the rotor blade and the tubular tower are successfully detected by applying the Principal Component Analysis (PCA) based methods to these temperature-sensitive frequency estimates. Comparison of change of the extracted health features indicates that they are more sensitive with the tower damage.
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.
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.
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.
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.
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.
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.
A prototype of wind turbines in 5 megawatt dass was built and tested at the first German offshore wind energy test fteld in the North Sea. In order to investigate dynamic behaviors under a complex state of loads, a continuous dynamic monitoring System was implemented by Federal Institute for Material Research and Testing (BAM). It recorded structural responses and environmental/operational variables from November 2007 to October 2009.
This paper presents significant resonance phenomenon due to the interaction in the tower-nacelle System under operational conditions. Modal parameters are automatically estimated by the poly reference Least Square Complex Frequency domain (p-LSCF) method. Campbell plot demonstrates that a three-blade passage frequency and its multiples f3n match with the natural frequencies of the wind turbine System in several modal Orders. The damping estimates decrease and the Vibration amplitude increase significantly. A control System is necessary to minimize the excessive vibrations.
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.
We introduce a new concept that enables a decision analyst to explore and quantify the benefits of decision alternatives that exceed the scope of a pre‐posterior decision or value of information analysis. This new concept, namely, the expected value of sample information and action analysis, facilitates to examine decision alternatives that become only possible with additional knowledge. The concept is introduced by taking basis in proof load testing as a source of (pre‐)posterior knowledge. Pre‐posterior decision analysis is necessary in order to optimize the structural design through proof loading information. The application of the common value of information Analysis and the new value of information and action analysis are demonstrated in a case study.
Risk Assessment and Value of Action Analysis for Icing Conditions of Wind Turbines Close to Highways
(2019)
The paper presents research results from the Marie Skłodowska-Curie Innovative Training Network INFRASTAR in the field of reliability approaches for decision-making for wind turbines and bridges. This paper addresses the application of Bayesian decision analysis for installation of heating systems in wind turbine blades in cases where an ice detection system is already installed in order to allow wind turbines to be placed close to highways. Generally, application of ice detection and heating systems for wind turbines is very relevant in cases where the wind turbines are planned to be placed close to urban areas and highways, where risks need to be considered due to icing events, which may lead to consequences including human fatality, functional disruptions, and/or economic losses. The risk of people being killed in a car passing on highways near a wind turbine due to blades parts or ice pieces being thrown away in cases of overicing is considered in this paper. The probability of being killed per kilometer and per year is considered for three cases: blade parts thrown away as a result of a partial or total failure of a blade, ice thrown away in two cases, i.e., of stopped wind turbines and of wind turbines in operation. Risks due to blade parts being thrown away cannot be avoided, since low strengths of material, maintenance or manufacturing errors, mechanical or electrical failures may result in failure of a blade or blade part. The blade (parts) thrown away from wind turbines in operation imply possible consequences/fatalities for people near the wind turbines, including in areas close to highways.
Similar consequences are relevant for ice being thrown away from wind turbine blades during icing situations. In this paper, we examine the question as to whether it is valuable to put a heating System on the blades in addition to ice detection systems. This is especially interesting in countries with limited space for placing wind turbines; in addition, it is considered if higher power production can be obtained due to less downtime if a heating system is installed.
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.
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.
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.
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.
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.
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.
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.