Geotechnical correlation field-informed and data-driven prediction of spatially varying geotechnical properties

Chen, W., Ding, J., Shi, C. et al. (2 more authors) (2024) Geotechnical correlation field-informed and data-driven prediction of spatially varying geotechnical properties. Computers and Geotechnics, 171. 106407. ISSN 0266-352X

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Item Type: Article
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© 2024, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. This is an author produced version of an article published in Computers and Geotechnics. Uploaded in accordance with the publisher's self-archiving policy.

Keywords: Spatial variability; Data-driven Method; Random field theory; Site investigation; Neural Network
Dates:
  • Published: July 2024
  • Published (online): 10 May 2024
  • Accepted: 6 May 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 21 Oct 2024 09:40
Last Modified: 21 Oct 2024 09:42
Published Version: https://www.sciencedirect.com/science/article/pii/...
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.compgeo.2024.106407
Open Archives Initiative ID (OAI ID):

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Filename: Geotechnical Correlation Field-informed .pdf

Licence: CC-BY-NC-ND 4.0

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