Report for ISSMGE TC309/TC304/TC222 and ASCE Geo-Institute Risk Assessment and Management Committee Fourth Machine Learning in Geotechnics Dialogue on “Machine Learning Supremacy Projects”-Reference-Cited by-同舟云学术

Report for ISSMGE TC309/TC304/TC222 and ASCE Geo-Institute Risk Assessment and Management Committee Fourth Machine Learning in Geotechnics Dialogue on “Machine Learning Supremacy Projects”

Published:2024-01-02 Issue:1 Volume:18 Page:304-313
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:

Leung Andy Y.F.¹^ORCID,Phoon Kok-Kwang²^ORCID,Xiao Te³^ORCID,Shuku Takayuki⁴^ORCID,Ching Jianye⁵^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

2. Information Systems Technology and Design/Architecture and Sustainable Design, Singapore University of Technology and Design, Singapore, Singapore

3. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, People’s Republic of China

4. Faculty of Environmental, Life, Natural Science and Technology, Okayama University, Okayama, Japan

5. Department of Civil Engineering, National Taiwan University, Taipei, Taiwan

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17499518.2024.2316879

Reference38 articles.

1. AGS. 2022. “Electronic Transfer of Geotechnical and Geoenvironmental Data AGS4 Edition 4.1.1.” https://www.ags.org.uk/content/uploads/2022/02/AGS4-v-4.1.1-2022.pdf.

2. Ching, J. 2024. Bayesian Machine Learning in Geotechnical Site Characterization. Boca Raton: CRC Press.

3. Eurocode 7. 2004. Geotechnical Design – Part 1: General Rules. Brussels: European Committee for Standardisation.

4. Editorial

5. Hayashi K. 2022. “Application of Neural Network to Analysis and Interpretation of Geophysical Data.” Proceedings of the 147th SEGJ Conference (in Japanese).

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1. Future of Machine Learning in Geotechnics (FOMLIG), 5–6 Dec 2023, Okayama, Japan;Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards;2024-01-02

2. Correction;Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards;2024-01-02

3. Special Issue on “Data-Centric Geotechnics for Practice”;Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards;2024-01-02

4. Digging Deeper: The Role of Big Data Analytics in Geotechnical Investigations;E3S Web of Conferences;2024