| Summary: | Abstract The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (V S30), the topographical slope (slope), the fundamental resonance frequency (f 0) and the depth beyond which V s exceeds 800 m/s (H 800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [V S30–f 0], [V S30–H 800], [f 0–slope], [H 800–slope], [V S30–slope] and [f 0–H 800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA (T), and M w, R JB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median ground-motion prediction, it does impact the level of aleatory uncertainty. V S30 is found to perform the best of single proxies at short periods (T < 0.6 s), while f 0 and H 800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [V S30–H 800] and [f 0–slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the “stiff” spectral ordinate at the considered period. Graphical Abstract .
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