Examining the contribution of near real-time data for rapid seismic loss assessment of structures

Enrico Tubaldi (1) , Ekin Ozer (1) , John Douglas (1) , Pierre Gehl (2)

1 University of Strathclyde [Glasgow]
2 BRGM - Bureau de Recherches Géologiques et Minières (BRGM)

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.

Mots clés: Bayesian networks; Bayesian updating; multisensory data fusion; probabilistic seismic demand modelling; structural health monitoring

Domaines: Sciences de la Terre

Enrico Tubaldi, Ekin Ozer, John Douglas, Pierre Gehl. Examining the contribution of near real-time data for rapid seismic loss assessment of structures. Structural Health Monitoring, 2022, 21 (1), pp.118-137. ⟨10.1177/1475921721996218⟩. ⟨hal-03654335⟩