Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response

dc.contributor.author	Sanger, Morgan D.
dc.contributor.author	Geyin, Mertcan
dc.contributor.author	Maurer, Brett W.
dc.date.accessioned	2026-08-20T10:56:09Z
dc.date.available	2026-08-20T10:56:09Z
dc.date.copyright	2025/08/23
dc.date.issued	2025
dc.identifier.other	JGGEFK.GTENG-13737.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311466
dc.description.abstract	AbstractUsing machine learning (ML), high performance computing, and a large body of geospatial information, we develop surrogate models to predict soil liquefaction across regional scales. Two sets of models—one global and one specific to New Zealand—are ...
dc.publisher	American Society of Civil Engineers
dc.title	Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response
dc.type	Journal Article
dc.journal.volume	151
dc.journal.issue	11
dc.journal.title	Journal of Geotechnical and Geoenvironmental Engineering
dc.identifier.doi	10.1061/JGGEFK.GTENG-13737
dc.journal.fristpage	04025126-1
dc.journal.lastpage	04025126-16
dc.page	16
dc.tree	Journal of Geotechnical and Geoenvironmental Engineering:;2025:;Volume ( 151 ):;issue: 011
dc.contenttype	Fulltext