Evaluation of building collapse risk and drift demands by nonlinear structural analyses using conventional hazard analysis versus direct simulation with cybershake seismograms

Limited data on strong earthquakes and their effect on structures pose challenges of making reliable risk assessments of tall buildings. For instance, although the collapse safety of tall buildings is likely controlled by large-magnitude earthquakes with long durations and high low-frequency content, there are few available recorded ground motions to evaluate these issues. The influence of geologic basins on amplifying ground-motion effects raises additional questions. Absent recorded motions from past large magnitude earthquakes, physics-based ground-motion simulations provide a viable alternative. This article examines collapse risk and drift demands of a 20-story archetype tall building using ground motions at four sites in the Los Angeles (LA) basin. Seismic demands of the building are calculated form nonlinear structural analyses using large datasets (∼500;000 ground motions per site) of unscaled, site-specific simulated seismograms. Seismic hazard and building performance from direct analysis of Southern California Earthquake Center CyberShake motions are contrasted with values obtained based on conventional approaches that rely on recorded motions coupled with probabilistic seismic hazard assessments. At the LA downtown site, the two approaches yield similar estimates of mean annual frequency of collapse (λc), whereas nonlinear drift demands estimated with direct analysis are slightly larger primarily because of differences in hazard curves. Conversely, at the deep basin site, the CyberShake-based analysis yields around seven times larger λc than the conventional approach, and both hazard and spectral shapes of the motions drive the differences. Deaggregation of collapse risk is used to identify the relative contributions of causal earthquakes, linking building responses with specific seismograms and contrasting collapse risk with hazard. A strong discriminative power of average spectral acceleration and significant duration for predicting collapse is observed.