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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.
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.