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- Value of information (6)
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#### Organisationseinheit der BAM

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 effects of the deterioration on the value of damage detection information. The quan-tification of the value of damage detection information for deteriorated structures is based on Bayesian pre-posterior decision analysis, comprising structural system performance models, consequence, benefit and costs models and damage detection information models throughout the service life of a structural system. The value of damage detection information accounts for the relevance and precision of the information to ensure the structural integrity and to reduce the potential structural system risks and expected costs throughout the ser-vice life before implementing damage detection system. With the developed approach, the value of damage detection information for a statically determinate Pratt truss bridge girder subjected to different deterioration models is calculated. The analysis shows the impact of the deterioration model parameters on the value of damage detection information. The results can be used to develop optimal maintenance strategies before im-plementation of the damage detection system.

Applications of Structural Health Monitoring (SHM) for the design and analysis of Offshore Wind Turbine (OWT) structures has gained much attention within the past few years. Being exposed to repeated cyclic wind and wave loads, OWTs are dynamically sensitive structures and can benefit from monitoring systems to predict time-dependent deterioration. This study focuses on the quantification of the value of SHM information on the maintenance of OWT structures, with emphasis on fatigue of welded joint. By utilizing the decision tree, structural reliability, SHM data, as well as the cost-benefit assessments, a value of information (VoI) analysis can be done to model the fundamental decision of whether the service life of an OWT foundation can be extended beyond the initial design life. The application is demonstrated on a butt weld of a monopile support structure of a 3 MW offshore wind turbine with a hub height of approximately 71m where the prior probability analysis is modelled as a probabilistic fatigue damage model based on S-N approach and designed wind data. The posterior probability of failure of welded joints is updated using the three-year measured oceanographic and one-year strain data. The expected value of SHM information can be found as the difference between the maximum utility obtained in posterior analysis with SHM information and the maximum utility obtained using only prior information. This work can provide insights on how much benefits can be achieved through SHM information, with practical relevance on reliability-based design optimization and fatigue life extension of OWT structures.

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.

On 14 August 2018, the Morandi bridge in Genoa Italy collapsed, which caused 43 people to die and great losses. Here I am not going to talk about why it collapsed. But I want to say what we can do to prevent this kind of tragedy from happening. Any structures are like us, if we are sick, we go to doctors. But if structures are sick, how could we know?
Yes, through structural health monitoring-SHM! SHM is process of implementing damage detection strategy, which is like a doctor giving us indication of the structure health status. However, the SHM is not like our health insurance which can be free. Some structure owners may be hesitated on investing large money before they see the VALUE.
Although it is proved that SHM is beneficial, the existing literature doesn’t tell them: when to do the monitoring? How often to do that? Which SHM techniques to choose? After monitoring, what action to take? There are so many decisions needed to be made, that’s why here comes my research, which can help to answer all these questions by breaking the wall of value of monitoring information!
So how do we do that? Here we introduce a decision tree. A Decision tree is a decision support tool that uses a tree-like graph or model to describe decisions and their possible outcomes, including how likely it will happen, how severe the consequences will be. Based on the probabilities and their respective costs and benefits, the value of every decision can be quantified.
Based on the decision trees, different SHM strategies can be compared and the optimal strategy will be the one with highest value. After Knowing the value of SHM, we can improve the decision basis for design, operation and life-cycle integrity management of structures to reduce the risk, reduce the cost and extend service life benefits.
So that everyone in society can benefit from a safer and more reliable environment!

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 method to determine the structural and damage detection system influencing parameters 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 SHM 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 damage detection system performance influencing parameters including number of sensors, sensor locations, measurement noise and the Type I error are investigated. The pre-posterior probabilistic model is computed utilizing the Bayes’ theorem to update the prior system model with the damage indication information. Finally, the value of information 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 higher the deterioration rate is, the more it is beneficial to do SHM. Furthermore, it shows that more sensors do not necessarily lead to a higher value of information; only specific sensor locations near the highest utilized components lead to a high value of information; measurement noise and the Type I error should be controlled and as small as possible. An optimal sensor employment with highest 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.

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 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.