Combining observed linear basin amplification factors with 1D nonlinear site-response analyses to predict site response for strong ground motions: Application to Wellington, New Zealand

Abstract

This study develops a method for estimating site amplification that combines instrumentally observed site-specific amplification factors with adjustment factors from nonlinear site-response analyses. This approach provides estimates of site response for large-strain motions based on observations and sophisticated nonlinear modeling. A database of weak-to-moderate intensity ground motions recorded in three basins of Wellington, New Zealand is used to study the observed site amplification. A subset of nine strong-motion stations was selected to perform nonlinear site-response analyses with scaled strong ground motions to assess the influence of nonlinearity on site amplification factors and demonstrate the approach. Different shear-wave velocity ( VS) profiles, constitutive models, and modeling approaches (e.g. one-dimensional (1D) site-response analyses vs empirical [Formula: see text]-based approaches) are used to quantify the sensitivity and modeling uncertainty in the nonlinear site-response analyses. It was found that for soft sites subjected to strong ground motions, there may be a decrease in spectral acceleration amplification factors for periods up to approximately 2 s, relative to the expected linear site response. For longer periods, there is little to no amplification from the effects of soil nonlinearity. However, at stiffer sites, which generally experience less basin amplification in observations, there may be moderate amplification at longer periods when nonlinearity is considered due to softening of the soil profile. Empirical ground-motion models were found to under-represent the observed amplification between basin sites and the nearby reference site, especially at intermediate to long periods, corresponding to resonant frequencies of these basin sites. In addition, the empirical nonlinear site amplification models ([Formula: see text]-based) were found to deviate from nonlinear analyses at large strains, where such models are poorly constrained due to such a limited number of observations.