Rapid Prediction of Infrastructure Damage and Loss Due to Earthquake-Induced Soil Liquefaction

Abstract

Semi-empirical models based on in-situ geotechnical tests have been the standard-of-practice for predicting soil liquefaction since 1971. Recently, geospatial prediction models utilizing free, readily-available data were proposed using satellite remote-sensing to infer subsurface traits without in-situ tests. Using 15,222 liquefaction case-histories from 24 earthquakes, this study assesses the performance of 23 models based on geotechnical or geospatial data using standardized metrics. Uncertainty due to finite sampling of case histories is accounted for and used to establish statistical significance. Geotechnical predictions are significantly more efficient on a global scale, yet successive models proposed over the last twenty years show little demonstrable improvement. In addition, geospatial models perform equally well, or better, for large subsets of the data – a provocative result considering the relative time- and cost-requirements underlying these predictions. Given the demonstrated potential of Geospatial models to predict soil liquefaction, efforts are made to extend the use of these models to also predict the ensuing infrastructure damage and loss. Towards this end, the present study focuses on structures built atop shallow foundation systems. Utilizing damage-survey data and insurance loss-assessments for 62,000 such assets, functions for predicting liquefaction-induced damage and loss in near real-time are developed.