Quantification of the value of monitoring information for deteriorated structures

Abstract

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.