Determination of structural and damage detection system influencing parameters on the value of information

International audienceA method to determine the influencing parameters of a structural and Damage Detection System (DDS) is proposed based on the Value of Information (VoI) analysis. The VoI analysis utilizes the Bayesian pre-posterior decision theory to quantify the value of DDS 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 DDS performance, 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 DDS 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 VoI over the whole service life. Furthermore, it shows that more sensors do not necessarily lead to a higher relative VoI; only specific sensor locations near the highest utilized components lead to a high relative VoI; measurement noise and the Type I error should be controlled and be as small as possible. An optimal sensor employment with highest relative VoI 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 DDS settings and sensor configuration for minimum expected costs and risks