Infrastructure systems such as bridges are one of the most crucial and integral parts of civilization and substantially affect a nation’s economic, social, and strategic solidity. While the importance of their safe operation is quite clear given their role in the welfare of society and sustainable societal development, the durability of the required safety levels and the quality of European bridge stock1 have been questioned over time due to catastrophic events such as the collapse of the Morandi Bridge in Genoa, Italy, in 2018. Therefore, maintenance and integrity management of those infrastructures is of great importance to mitigate the risk imposed by possible future catastrophic events.
The goal of the ObSerVation is to develop a unified decision support tool (DST) for the design of monitoring systems for integrity management of bridges and bridge networks in regions highly prone to seismic activity. The DST will be based on the Value of Information (VoI) theory and will exploit information about the capacity of the structure gained through Structural Health Monitoring (SHM) data and information about the seismic demand acquired through measurements and state-of-the-art ground motion (GM) simulations.
The idea of the ObSerVation is to model the effect of damage accumulation, as well as the effect of the ground motion-specific properties on the structural response, exploiting recent developments presented in the literature. Thus, advanced deterioration models that account for the continuous nature of both gradual and sudden deterioration processes based on the information from the SHM will be incorporated into the integrity management framework based on the Value of Information analysis. In addition, advanced ground motion modelling through the 3D Physics-Based Simulation will be incorporated into the integrity management framework to account for the source- (e.g., stress drop), path- (e.g., focusing), and site-dependent (e.g., topography) properties of the GM, instead of employing the conventional approach that relies on the Ground Motion Prediction Equations to estimate the level of shaking due to an earthquake.