Future of machine learning in geotechnics-Reference-Cited by-同舟云学术

Future of machine learning in geotechnics

Published:2022-06-16 Issue:1 Volume:17 Page:7-22
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,Zhang Wengang²

Affiliation:

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

2. School of Civil Engineering, Chongqing University, Chongqing, People’s Republic of China

Publisher

Informa UK Limited

Subject

Geology,Geotechnical Engineering and Engineering Geology,Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/17499518.2022.2087884

Reference77 articles.

1. 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.

2. American Society of Civil Engineers. 2019. Future World Vision: Infrastructure Reimagined. https://cdn.asce.org/fwv/files/pdfs/asce-future-world-vision-final-report-updated-may-2019.pdf

3. Geotechnical BIM in 2020

4. Image-Data-Driven Slope Stability Analysis for Preventing Landslides Using Deep Learning

5. The implementation and role of geotechnical data in BIM process

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