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Summary Amplitude scaling is commonly used to select ground motions matching a target response spectrum. In this paper, the effect of scaling limits on ground motion selection, based on the conditional spectrum framework, is investigated. Target spectra are computed for four probabilistic seismic hazard cases in Western United States, and 16 ground motion suites are selected using different scaling limits (ie, 2, 5, 10, and 15). Comparison of spectral acceleration distributions of the selected ground motion suites demonstrates that the use of a scaling limit of 2 yields a relatively poor representation of the target spectra, because of the small limit leading to an insufficient number of available ground motions. It is also shown that increasing scaling limit results in selected ground motions with generally increased distributions of Arias intensity and significant duration Ds5‐75, implying that scaling limit consideration can significantly influence the cumulative and duration characteristics of selected ground motions. The ground motion suites selected are then used as input for slope displacement and structural dynamic analyses. Comparative results demonstrate that the consideration of scaling limits in ground motion selection has a notable influence on the distribution of the engineering demand parameters calculated (ie, slope displacement and interstory drift ratio). Finally, based on extensive analyses, a scaling limit range of 3 to 5 is recommended for general use when selecting ground motion records from the NGA‐West2 database.

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Ground motion intensity measures (IMs) were observed to be spatially correlated during past earthquakes. In this article, a new spatial cross-correlation model for a vector-IM, which consists of spectral acceleration (SA) ordinates at 17 periods and six non-SA IMs (e.g. peak ground velocity, Arias intensity, cumulative absolute velocity, and significant durations), is proposed using principal component analysis (PCA) and geostatistical analysis. A total of 3797 ground motion records are selected from the NGA-West2 database for such analyses. PCA is used to transform the spatially correlated within-event residuals into uncorrelated principal components; a permissible function is then proposed to fit the empirical semivariograms calculated by the principal components. It is evident that the proposed model performs well in capturing the spatial variability characteristics of the multiple ground motion IMs. A simple example is presented to illustrate the use of the proposed model in realizing spatially correlated ground motion residuals of multiple IMs. The model developed enables one to simulate spatially cross-correlated IMs over a large area in a rapid way.

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