| contributor author | Sanger, Morgan D. | |
| contributor author | Geyin, Mertcan | |
| contributor author | Maurer, Brett W. | |
| date accessioned | 2026-08-20T10:56:09Z | |
| date available | 2026-08-20T10:56:09Z | |
| date copyright | 2025/08/23 | |
| date issued | 2025 | |
| identifier other | JGGEFK.GTENG-13737.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4311466 | |
| 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response | |
| type | Journal Article | |
| journal volume | 151 | |
| journal issue | 11 | |
| journal title | Journal of Geotechnical and Geoenvironmental Engineering | |
| identifier doi | 10.1061/JGGEFK.GTENG-13737 | |
| journal fristpage | 04025126-1 | |
| journal lastpage | 04025126-16 | |
| page | 16 | |
| tree | Journal of Geotechnical and Geoenvironmental Engineering:;2025:;Volume ( 151 ):;issue: 011 | |
| contenttype | Fulltext | |
