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The severity of surficial liquefaction manifestation was significantly over-predicted for a large subset of case histories from relatively recent earthquakes that impacted the Canterbury region of New Zealand. Such over-predicts generally occurred for profiles having predominantly high fines-content (FC), high-plasticity soil strata. Herein, the liquefaction case histories from the Canterbury earthquakes are used to investigate the performances of three different manifestation severity index (MSI) models. The prevalence of high FC, high-plasticity strata in a profile is quantified through the soil behavior type index averaged over the upper 10 m of a profile ( I_c10). It is shown that for each MSI model (1) the threshold MSI value distinguishing cases with and without manifestation increases as I_c10increases and (2) the ability of the MSI to segregate cases with and without manifestation decreases with increasing I_c10. Additionally, probabilistic models are proposed for evaluating the severity of surficial liquefaction manifestation as a function of MSI and I_c10. The approaches presented in this study allow better interpretations of predictions made by existing MSI models, although their efficacy decreases at sites with high I_c10. An improved MSI model is ultimately needed that better accounts for the effects of high-FC, high-plasticity soils more directly. 

ABSTRACT A probabilistic seismic hazard analysis performed for rock conditions and modified for soil conditions using deterministic site amplification factors does not account for uncertainty in site effects, which can be significant. One approach to account for such uncertainty is to compute a weighted average amplification curve using a logic tree that accounts for several possible scenarios with assigned weights corresponding to their relative likelihood or confidence. However, this approach can lead to statistical smoothing of the amplification curve and possibly to decreased computed hazard as epistemic uncertainty increases. This is against the expected trend that higher uncertainty leads to higher computed hazard, thus reducing the incentive for practitioners to characterize soil properties at a site. This study proposes a modified approach in which the epistemic uncertainty is captured in a plot of amplification factors versus period. Using a case history, the proposed method is shown to improve the issue with the weighted average method for at least two oscillator periods and to yield similar results for two other periods in which the highlighted issue is less significant.