Machine learning-based models for estimating liquefaction-induced building settlements-Reference-Cited by-同舟云学术

Machine learning-based models for estimating liquefaction-induced building settlements

Published:2024-07 Issue: Volume:182 Page:108673
ISSN:0267-7261
Container-title:Soil Dynamics and Earthquake Engineering
language:en
Short-container-title:Soil Dynamics and Earthquake Engineering

Author:

Liu Chenying^ORCID,Macedo Jorge^ORCID

Funder

Georgia Institute of Technology

National Science Foundation

Publisher

Elsevier BV

Reference46 articles.

1. Bouziou D, O’Rourke T, Cubrinovski M, Henderson D. Evaluation of ground deformations during the 2010–2011 Canterbury earthquake sequence. In: Proceedings of the 6th international conference on earthquake geotechnical engineering. 2015, p. 1–4.

2. Characteristics of liquefaction in Tokyo Bay area by the 2011 Great East Japan earthquake;Yasuda;Soils Found,2012

3. Performance-based probabilistic assessment of liquefaction-induced building settlements;Liu;Soil Dynam Earthq Eng,2021

4. 6Th Ishihara lecture: Simplified procedure for estimating liquefaction-induced building settlement;Bray;Soil Dynam Earthq Eng,2017

5. A physics-informed semi-empirical probabilistic model for the settlement of shallow-founded structures on liquefiable ground;Bullock;Géotechnique,2018

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