Volume 21, Issue 6

Search all Journal of Zhejiang University-SCIENCE A articles

June 2020

Special Issue on Practice of Artificial Intelligence in Geotechnical Engineering Guest Editor-in-Chief: Zhen-yu YIN Guest Editors: Yin-fu JIN, Zhong-qiang LIU

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7 articles in this issue

Practice of artificial intelligence in geotechnical engineering
Zhen-yu Yin
Yin-fu Jin
Zhong-qiang Liu
Editorial 16 June 2020 Pages: 407 - 411
Algorithms for intelligent prediction of landslide displacements
Zhong-qiang Liu
Dong Guo
Jung-chan Choi
OriginalPaper 16 June 2020 Pages: 412 - 429
Machine learning-based prediction of soil compression modulus with application of 1D settlement
Dong-ming Zhang
Jin-zhang Zhang
Chen-yu Chang
OriginalPaper 16 June 2020 Pages: 430 - 444
Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests
Cristian Godoy
Ivan Depina
Vikas Thakur
OriginalPaper Open access 16 June 2020 Pages: 445 - 461
Estimation of spatiotemporal response of rooted soil using a machine learning approach
Zhi-liang Cheng
Wan-huan Zhou
Yong-xing Guo
OriginalPaper 16 June 2020 Pages: 462 - 477
Characterization of spatial variability with observed responses: application of displacement back estimation
Yi-xuan Sun
Lu-lu Zhang
Jun-yi Yan
OriginalPaper 16 June 2020 Pages: 478 - 495
A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data
Sheng-liang Lu
Ning Zhang
Hu-zhong Li
OriginalPaper 16 June 2020 Pages: 496 - 508

Volume 21, Issue 6

Practice of artificial intelligence in geotechnical engineering

Algorithms for intelligent prediction of landslide displacements

Machine learning-based prediction of soil compression modulus with application of 1D settlement

Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests

Estimation of spatiotemporal response of rooted soil using a machine learning approach

Characterization of spatial variability with observed responses: application of displacement back estimation

A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data

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