Value of Information Across Ground Motion Models

Abstract

Effective seismic emergency management depends on accurate seismic hazard analysis, where selecting a suitable Ground Motion Model (GMM) for the target region is a critical initial step. Although numerous GMMs are available, identifying the one that better captures the intensity measures characteristic of a target region remains nontrivial. This study investigates the effect of GMM selection on the Value of Information (VoI) obtained from ground motion monitoring. A case study is conducted for a bridge, considering multiple potential locations in Central Italy, with seismic demand estimated using two different GMMs applicable to the region. The results reveal that the difference in seismic hazard predictions between the two GMMs significantly affects the identification of the optimal emergency management actions and therefore the VoI. Using Log-Likelihood (LLH) method, to assess the statistical fit of each GMM against observed ground motions, one model is found to provide a better fit and a correspondingly higher VoI. These findings suggest that employing a statistically robust GMM can help avoid unnecessary infrastructure shutdowns and minimize the risk of underestimating seismic damage.