The Pacific Earthquake Engineering Research Center Next Generation Attenuation-West2 database is used to derive a new conditional ground-motion model (CGMM) and a set of scenario-based models for estimating cumulative absolute velocity (CAV) for earthquakes in shallow crustal tectonic settings. Random-effects regressions were performed to develop the conditional model, with random effects across different earthquakes. The estimate of CAV is conditioned on the estimated peak ground acceleration (PGA), the time averaged shear-wave velocity in the top 30 m (VS30), the earthquake magnitude (Mw), and the rupture distance (Rrup). By combining the conditional CAV model with ground-motion models (GMM) in shallow crustal earthquake zones for PGA, new scenario-based models are developed for estimating the median CAV and its standard deviation, directly from an earthquake scenario and site conditions. A scenario-based CAV model captures inherently the complex ground-motion scaling effects included in the GMMs for spectral accelerations on which it is based on, such as, sediment-depth effects, soil nonlinearity effects, and regionalization effects. This approach also ensures consistency between the estimated CAV values and a design spectral acceleration response spectrum. The conditional and scenario-based models to estimate CAV are presented, and trends of the developed scenario-based models and previous traditional models for CAV are compared. Interestingly, we found a remarkable consistency between scenario-based and traditional nonconditional CAV models, when the underlain spectral GMM used in the implementation of the scenario-based model is properly constrained. Finally, we provide examples for the use of the conditional and scenario-based models in performance-based earthquake engineering.

Distributed energy resources can enhance community resilience to power outages in the aftermath of natural disasters. This article presents a method to quantify the resilience value that rooftop solar systems can provide to residential neighborhoods. Homes are grouped into geographical clusters to simulate the effect of sharing energy when a disaster disables the electric grid and damages some of the homes. Historical energy consumption and solar irradiance data are used to estimate the likelihood that each cluster could meet its own energy needs, given a defined level and pattern of rooftop solar adoption. As a case study, the method is applied to single-family homes in San Carlos, California, subjected to a disaster scenario representing the 1906 San Francisco earthquake. The case study shows how higher rooftop solar adoption levels increase postearthquake power accessibility during different seasons of the year. It also demonstrates that policy intervention can ensure more geographically uniform solar adoption and, therefore, more even resilience. Finally, the article evaluates the effect and cost of such an intervention, finding that a modest subsidy can make a notable difference in evening out resilience across a community.

In this study, we develop a new nonergodic ground motion model (GMM) for Chile, which better captures the trade-off between the aleatory variability and epistemic uncertainty on ground motion estimates compared with existing GMMs. The GMM is developed for peak ground acceleration and pseudospectral acceleration at a period of 1 s. Most existing GMMs for subduction earthquake zones were developed based on an ergodic assumption, and this is not the exception for the subduction zone in Chile. Under the ergodic assumption, the ground motion variability at a given single site–source combination is considered the same as the variability observed in a global database. However, recent efforts have highlighted significant location-specific systematic and repeatable effects for ground motions recorded within a particular region. These systematic effects promote the relaxation of the ergodic assumption and the transition to the development of nonergodic GMMs. The nonergodic GMM developed in this study uses an ergodic GMM as a backbone, the systematic source and site effects are modeled using Gaussian processes, and the path effects are modeled using the cell-specific attenuation approach enhanced with a computer graphics-based algorithm. The coefficients of the nonergodic GMM are estimated using Bayesian inference via Markov chain Monte Carlo (MCMC) methods, in which we use an integrated nested Laplace approximation approach to address the computational burden involved in MCMC. The developed nonergodic GMM reveals spatially varying and correlated location-specific source, path, and site effects in Chile, which cannot be captured by existing Chilean ergodic GMMs. Moreover, the developed nonergodic GMM shows a reduced aleatory variability compared to existing ergodic GMMs that are commonly used in Chile. In addition, the developed nonergodic GMM shows small epistemic uncertainty for regions with large ground motion data and large epistemic uncertainty for regions with few ground motion data. Finally, we provide guidelines on how to use the developed nonergodic GMM in the context of probabilistic seismic hazard analysis, which is important for performance-based earthquake engineering assessments in Chile.

The PEER NGA-Sub ground-motion intensity measure database is used to develop new conditional ground-motion models (CGMMs), a set of scenario-based models, and non-conditional models to estimate the cumulative absolute velocity ([Formula: see text]) of ground motions from subduction zone earthquakes. In the CGMMs, the median estimate of [Formula: see text] is conditioned on the estimated peak ground acceleration ([Formula: see text]), the time-averaged shear-wave velocity in the top 30 m of the soil ([Formula: see text]), the earthquake magnitude ([Formula: see text]), and the spectral acceleration at the period of 1 s ([Formula: see text]). Multiple scenario-based [Formula: see text] models are developed by combining the CGMMs with pseudo-spectral acceleration ([Formula: see text]) ground-motion models (GMMs) for [Formula: see text] and [Formula: see text] to directly estimate [Formula: see text] given an earthquake scenario and site conditions. Scenario-based [Formula: see text] models are capable of capturing the complex ground-motion effects (e.g. soil non-linearity and regionalization effects) included in their underlying [Formula: see text]/[Formula: see text] GMMs. This approach also ensures the consistency of the [Formula: see text] estimates with a [Formula: see text] design spectrum. In addition, two non-conditional [Formula: see text] GMMs are developed using Bayesian hierarchical regressions. Finally, we present comparisons between the developed models. The comparisons show that if non-conditional GMMs are properly constrained, they are consistent with scenario-based GMMs. The [Formula: see text] GMMs developed in this study advance the performance-based earthquake engineering practice in areas affected by subduction zone earthquakes.

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