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Articles

Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response | Journal of Geotechnical and Geoenvironmental Engineering | Vol 151, No 11

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By Morgan Sanger, Mertcan Geyin, Brett W. Maurer
| ascelibrary.org
Data Availability Statement The geotechnical and geospatial data used in model development are all publicly available, as described and referenced in the text. The model products are available on DesignSafe, including: (1) global GLM geotiffs for LPI, LPIISH, and LSN (Sanger et al. 2024b); and (2) New Zealand GLM geotiffs for LPI, LPIISH, and LSN (Sanger et al. 2024c).
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Fragility Functions for Liquefaction-Induced Ground Failure

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By Mertcan Geyin, Brett W. Maurer
| ascelibrary.org
Downloaded 0 times Technical Papers Abstract The predicted severity of liquefaction manifested at the ground surface is a popular and pragmatic proxy of damage potential for infrastructure. Toward this end, the liquefaction potential index (LPI) and similar models are commonly used, and often codified, to predict surface manifestations on level ground.
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