Impact of partially non-ergodic site-specific probabilistic seismic hazard on risk assessment of single buildings

The growth of global ground-motion databases has allowed generation of non-ergodic ground-motion prediction equations (GMPEs) based on specific on-site recordings. Several studies have investigated the differences between the hazard estimates from ergodic versus non-ergodic GMPEs. Here instead we focus on the impact of non-ergodic PSHA estimates on the seismic risk of nonlinear single-degree-of-freedom systems representing ductile structures and compare it with the traditional risk estimates obtained using ergodic GMPEs. The structure-and-site-specific risk estimates depend not only on the difference in the hazard estimates but also on the different hazard-consistent ground-motion record selection that informs the response calculation. The more accurate structure-and-site-specific non-ergodic risk estimates show that traditional ones may be biased in a way impossible to predict a priori. Hence, the use of the non-ergodic approach is recommended, whenever possible. However, further advancements of non-ergodic GMPEs are necessary before being routinely utilized in real-life risk assessment applications.

Ground Motion Record Selection using Multi-objective Optimization Algorithms: A Comparative Study

Performing time history dynamic analysis using site-specific ground motion records according to the increasing interest in the performance-based earthquake engineering has encouraged studies related to site-specific Ground Motion Record (GMR) selection methods. This study addresses a ground motion record selection approach based on three different multi-objective optimization algorithms including Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II). The method proposed in this paper selects records efficiently by matching dispersion and mean spectrum of the selected record set and target spectrums in a predefined period. Comparison between the results shows that NSGA II performs better than the other algorithms in the case of GMR selection.