Influence of spatial correlation of strong ground motion on uncertainty in earthquake loss estimation

Abstract

In addition to the mean values of possible loss during an earthquake, parameters of the probability distribution function for the loss to a portfolio (e.g. fractiles and standard deviation) are very important. Recent studies have shown that the proper treatment of ground-motion variability and, particularly, the correlation of ground motion are essential for the estimation of the seismic hazard, damage and loss for distributed portfolios. In this study, we compared the effects of variations in the between-earthquake correlation and in the site-to-site correlation on seismic loss and damage estimations for the extended objects (hypothetical portfolio) and critical elements (e.g. bridges) of a network. A scenario earthquake approach and a portfolio containing a set of hypothetical building and bridges were used for the purpose. We showed that the relative influences of the types of correlation on characteristics of loss distribution and the probability of damage are not equal. In some cases, when the median values of loss distribution or the probability that at least one critical element of a lifeline will be damaged are considered and when the spatial correlation of ground motion is used, the possible variations in the between-earthquake correlation may be neglected. The shape of the site-to-site correlation function (i.e. the rate of decrease of the coefficient of spatial correlation with separation distance) seems also to be important when modelling spatially correlated ground-motion fields.

Spatial correlation of ground motions in estimating seismic hazards to civil infrastructure

“…Multiple earthquakes and loss probability curves were analyzed by Wesson and Perkins (2001), McVerry et al (2004), Bommer and Crowley (2006), Park et al (2007), Goda andHong (2008b, 2009), and Sokolov and Wenzel (2011b). Seismic loss estimation was performed using the techniques based on different ground-motion parameters, namely: modifi ed Mercalli intensities (McVerry et al, 2004;Molas et al, 2006), peak ground acceleration (Wesson and Perkins, 2001;Sokolov and Wenzel, 2011a,b), and spectral acceleration or spectral displacement (Lee et al, 2004;Crowley and Bommer, 2006;Lee and Kiremidjian, 2007;Park et al, 2007;Crowley et al, 2008a;Goda and Hong, 2008b), and using various descriptions of loss, i.e, the mean damage ratio (Wesson and Perkins, 2001;Lee et al, 2004;Crowley and Bommer, 2006;Lee and Kiremidjian, 2007;Crowley et al, 2008a;Goda and Atkinson, 2009;Sokolov and Wenzel, 2011b) and monetary loss (McVerry et al, 2004;Park et al, 2007;Goda andHong, 2008b, 2009;Sokolov and Wenzel, 2011a).…”

“…Several studies analyzed the infl uence of ground-motion uncertainty and correlation on characteristics of loss distribution for widely located building assets (portfolios) and spatially distributed structures (lifelines). Specifi c (scenario) earthquakes were considered by Lee et al (2004), Lee and Kiremidjian (2007), Molas et al (2006), Goda andAtkinson (2009), Crowley et al (2008a,b), and Sokolov and Wenzel (2011a). Multiple earthquakes and loss probability curves were analyzed by Wesson and Perkins (2001), McVerry et al (2004), Bommer and Crowley (2006), Park et al (2007), Goda andHong (2008b, 2009), and Sokolov and Wenzel (2011b).…”

“…An earthquake of magnitude 7.0, which located near the area, was considered and peak ground acceleration, together with the building-specifi c fragility curves, was used to evaluate ground shakinginduced structural damage. Details of the model and procedure may be found in Sokolov and Wenzel (2011a). For a given ground-motion amplitude generated for a cell, a single value of expected direct loss EDL i,j was estimated for every building.…”

“…bridges) of a network were analyzed by Sokolov and Wenzel (2011a) for a single (scenario) earthquake and by Sokolov and Wenzel (2011b) for multiple earthquakes. They showed that the impact depends on the hazard and probability level of interest (return period).…”

“…all the variability between earthquakes (ρ η = 1.0) and all the variability within earthquakes (ρ η = 0.0; ρ(Δ) = 0.0 for Δ > 0 km). The loss estimations were performed for a set of hypothetical buildings mimicking a real building stock, which was constructed based on three types of buildings that are typical for Taiwan 3.3 Characteristics of loss distribution (Probability Distribution Functions, PDF, and Cumulative Probability Functions, CPF) for the extreme cases of ground motion correlation estimated using 10 000 simulations, single earthquake (Sokolov and Wenzel, 2011a).…”