Future of Machine Learning in Geotechnics (FOMLIG), 5–6 Dec 2023, Okayama, Japan-Reference-Cited by-同舟云学术

Future of Machine Learning in Geotechnics (FOMLIG), 5–6 Dec 2023, Okayama, Japan

Published:2024-01-02 Issue:1 Volume:18 Page:288-303
ISSN:1749-9518
Container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
language:en
Short-container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards

Author:

Phoon Kok-Kwang¹^ORCID,Shuku Takayuki²

Affiliation:

1. Singapore University of Technology and Design, Singapore, Singapore

2. Okayama University, Okayama, Japan

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17499518.2024.2316882

Reference156 articles.

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3. Abubakar A. M. J. Brædstrup H. Di A. T. Diaz S. Freeman S. Hviid K. H. Karkov et al. 2021. Deep learning applications for wind farms site characterization and monitoring. SEG Technical Program Expanded Abstracts 3009-3013.

4. Akbas S. O. 2007. “Deterministic and Probabilistic Assessment of Settlements of Shallow Foundations in Cohesionless Soils.” Ph.D. Thesis. Department of Civil and Environmental Engineering Cornell University.

5. Improved Simplified Method for Prediction of Loads in Reinforced Soil Walls

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