期刊
EARTHQUAKE SPECTRA
卷 36, 期 3, 页码 1386-1411出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/8755293019899951
关键词
Soil liquefaction; prediction; geotechnical; geospatial; post-earthquake response; soil conditions
资金
- National Science Foundation (NSF) [CMMI-1751216]
- US Geological Survey (USGS) [G18AP-00006]
- Pacific Earthquake Engineering Research Center (PEER) [1132-NCTRBM]
- New Zealand Earthquake Commission (EQC)
Semi-empirical models based on in situ geotechnical tests have been the standard-of-practice for predicting soil liquefaction since 1971. More recently, prediction models based on free, readily available data were proposed. These geospatial models rely on satellite remote-sensing to infer subsurface traits without in situ tests. Using 15,223 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 20 years show little or no demonstrable improvement. In addition, geospatial models perform equally well for large subsets of the data-a provocative finding given the relative time- and cost-requirements underlying these predictions. Through this performance comparison, lessons for improving each class of model are elucidated in detail.
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