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The frequency content of an earthquake ground motion is important because it affects the dynamic response of earth and structural systems. Four scalar parameters that characterize the frequency content of strong ground motions are (1) the mean period (Tm), (2) the average spectral period (Tavg), (3) the smoothed spectral predominant period (To), and (4) the predominant spectral period (Tp). Tm and Tavg distinguish the low frequency content of ground motions, while To is affected most by the high frequency content. Tp does not adequately describe the frequency content of a strong ground motion and is not recommended. Empirical relationships are developed that predict three parameters (Tm, Tavg, and To) as a function of earthquake magnitude, site-to-source distance, site conditions, and rupture directivity. The relationships are developed from a large strong-motion database that includes recorded motions from the recent earthquakes in Turkey and Taiwan. The new relationships update those previously developed by the authors and others. The results indicate that three site classes, which distinguish between rock, shallow soil, and deep soil, provide a better prediction of the frequency content parameters and smaller standard error terms than conventional “rock” and “soil” site classes. Forward directivity significantly increases the frequency content parameters, particularly Tm and To, at distances less than 20 km. Each of the frequency content parameters can be predicted with reasonable accuracy, but Tm is the preferred because it best distinguishes the frequency content of strong ground motions.

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Site amplification models for Central and Eastern North America are developed from simulation results presented in a companion paper. Linear and nonlinear amplification functions are developed for response spectral (RS) accelerations and smoothed Fourier amplitude spectra (FAS). Linear RS model components include ground motion scaling with 30 m time-averaged shear wave velocity ( VS30 scaling) and the effects of site period and sediment depth. These models are modular and can be used with or without period or depth terms. Including these terms, especially site period, is desirable and improves model estimation. Modularity also allows linear and nonlinear amplification terms to be developed and combined with linear amplification models without bias. Nonlinear RS models reduce linear amplification as VS30 decreases and the intensity of rock outcrop motions increases. Linear FAS models are tabulated amplification values as functions of VS30 and depth; nonlinear FAS models are analogous to those for the RS. A linear model for correcting a VS30 = 760 m/s rock condition to VS = 3,000 m/s is produced.

Aleatory variability in ground-motion prediction, represented by the standard deviation (sigma) of a ground-motion prediction equation, exerts a very strong influence on the results of probabilistic seismic-hazard analysis (PSHA). This is especially so at the low annual exceedance frequencies considered for nuclear facilities; in these cases, even small reductions in sigma can have a marked effect on the hazard estimates. Proper separation and quantification of aleatory variability and epistemic uncertainty can lead to defensible reductions in sigma. One such approach is the single-station sigma concept, which removes that part of sigma corresponding to repeatable site-specific effects. However, the site-to-site component must then be constrained by site-specific measurements or else modeled as epistemic uncertainty and incorporated into the modeling of site effects. The practical application of the single-station sigma concept, including the characterization of the dynamic properties of the site and the incorporation of site-response effects into the hazard calculations, is illustrated for a PSHA conducted at a rock site under consideration for the potential construction of a nuclear power plant.

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Natural hazards engineering plays an important role in minimizing the effects of natural hazards 9 on society through the design of resilient and sustainable infrastructure. The DesignSafe 10 cyberinfrastructure has been developed to enable and facilitate transformative research in natural 11 hazards engineering, which necessarily spans across multiple disciplines and can take advantage 12 of advancements in computation, experimentation, and data analysis. DesignSafe allows researchers to more effectively share and find data using cloud services, perform numerical 14 simulations using high performance computing, and integrate diverse datasets such that researchers can make discoveries that were previously unattainable. This paper describes the design principles used in the cyberinfrastructure development process, introduces the main components of the DesignSafe cyberinfrastructure, and illustrates the use of the DesignSafe cyberinfrastructure in research in natural hazards engineering through various examples.