Developing region-specific fragility function for predicting probability of liquefaction induced ground failure

The liquefaction potential index (LPI) has been widely used to develop fragility function for predicting the liquefaction-induced ground failure. As the fragility function tends to vary from one region to another, it is best developed based on region-specific data. When the amount of region-specific data is limited, how to develop the region-specific fragility curve is a challenging problem. In this study, a Hierarchical Bayesian Model (HBM) is suggested for developing region-specific fragility functions based on LPI, which can systematically consider the effects of the amount and characteristics of the local data as well as the data from other regions. The suggested method is illustrated with an example. It is shown that the HBM outperforms the lumped parameter model (LPM) which does not consider the inter-region variability of the fragility curves. When the amount of region-specific data is large, the fragility function developed based on the HBM is very close to that developed based on the independent parameter model (IPM), which constructs a region-specific fragility function utilizing only the region-specific data. When the region-specific data is not enough, the HBM also outperforms the IPM through borrowing information from other regions.

Automated summary

In this study, a Hierarchical Bayesian Model (HBM) is proposed for developing region-specific fragility functions based on the liquefaction potential index (LPI). The HBM can systematically consider the effects of the amount and characteristics of the local data as well as data from other regions. The results show that the HBM outperforms the lumped parameter model (LPM) and can provide accurate fragility functions even with limited region-specific data.