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.
|Number of pages||10|
|Publication status||Published - 2018|
|Event||9th European Workshop on Structural Health Monitoring (EWSHM 2018) - Manchester, United Kingdom|
Duration: 10 Jul 2018 → 13 Jul 2018
|Conference||9th European Workshop on Structural Health Monitoring (EWSHM 2018)|
|Period||10/07/2018 → 13/07/2018|
Long, L., Thöns, S., & Döhler, M. (2018). The effects of SHM system parameters on the value of damage detection information. Paper presented at 9th European Workshop on Structural Health Monitoring (EWSHM 2018) , Manchester, United Kingdom.