Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources
Soil liquefaction assessment remains a crucial and complex challenge in seismic geotechnical engineering due to various liquefaction records and limited information, which entails a more generalized off-the-shelf model that can achieve favourable performance on different data sources. In this work,...
Saved in:
Main Authors: | Hongwei Guo, Chao Zhang, Hongyuan Fang, Timon Rabczuk, Xiaoying Zhuang |
---|---|
Format: | Article |
Language: | English |
Published: |
KeAi Communications Co., Ltd.
2025-08-01
|
Series: | Underground Space |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S246796742400134X |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Soil liquefaction assessment using machine learning
by: Gamze Maden Muftuoglu, et al.
Published: (2025-06-01) -
Characterization of Liquefaction Products from Lignocellulosic and Aquatic Biomass
by: Telma Moreira, et al.
Published: (2025-06-01) -
Liquefaction problems in geotechnical engineering.
Published: (1977) -
Highly efficient stacking ensemble learning model for automated keratoconus screening
by: Zahra J. Muhsin, et al.
Published: (2025-06-01) -
Plasticity, Flow Liquefaction, and Cyclic Mobility in Liquefiable Soils with Low to Moderate Plasticity
by: Carmine P. Polito, et al.
Published: (2025-06-01)