Decision support with Structural Health Monitoring

Adverse operational conditions, aging and deterioration are, among others, some of the main threats that structures and infrastructure systems are subjected to  throughout their life-cycle. The technological advancements in developing sensors, capable of providing diversified measurements of structural response (e.g. accelerations, strains, temperatures, loads, etc.), have lead to vast scientific and practical developments in the field of Structural Health Monitoring (SHM). Various techniques for translating the raw measurement data into indicators of structural “health” have been made readily available.

Despite these technological advancements, visual inspection still remains the primary, and oftentimes sole, means for condition-based assessment, in the current approach to infrastructure operation and maintenance. SHM systems may be exploited as a complementary source of information on the condition of a system and may serve for supporting decisions regarding the management of infrastructures throughout their life-cycle. However, it is currently difficult to quantify the effect of SHM on optimal operation and maintenance and hence on the total life-cycle cost. The goal of this project is the development of a framework, which employs efficient methods and tools, able to quantify and optimize the Value of Information (VoI) from the SHM systems.

Journal Publications

  • Antonios Kamariotis, Eleni Chatzi, Daniel Straub (2022), Value of information from vibration-based structural health monitoring extracted via Bayesian model updating, Mechanical Systems and Signal Processing 166,108465, https://doi.org/10.1016/j.ymssp.2021.108465

Conference Publications