U.S. National Liquefaction Hazard Maps and their Implications for Engineering Practice and Policy

Abstract

This study introduces U.S. national liquefaction hazard maps (NLHMs) developed using a mechanics-informed, geospatial machine learning model which surrogates state-of-practice liquefaction models, exploits a large library of geospatial predictors to infer subsurface conditions, and is anchored to measured conditions with in-situ test data. By convolving this geospatial liquefaction model with the 2023 U.S. national seismic hazard model, liquefaction hazard is mapped across the contiguous U.S. at ~90 m resolution within both conditional (2,475-year design event) and unconditional (return period of ground failure) formulations using high-performance computing for the high-resolution magnitude-disaggregation. The resulting NLHMs provide insights for land-use policy, preliminary site assessment, regional-scale earthquake simulation and response planning, and screening tools for regulatory enforcement, among other applications. Beyond quantifying and visualizing liquefaction hazard, the NLHMs are used herein to examine three questions of engineering practice and policy across a continuous spatial domain: (i) the effect of selecting modal versus mean magnitude in conditional analyses; (ii) the differences between conditional and unconditional hazard formulations; and (iii) the extent to which liquefaction hazard compounds with socioeconomic vulnerability. Results elucidate where and how the choice of magnitude alters computed hazards; that unconditional maps reveal important spatial deviations suppressed by single-scenario maps, which are convenient and widely used in current building codes, but less than completely rational; and that modest but statistically significant socioeconomic gradients in liquefaction exposure exist.