Investigation of deformation behaviour of uniaxially loaded sand grains using a novel high-resolution imaging apparatus and ensemble machine learning models-Reference-Cited by-同舟云学术

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Investigation of deformation behaviour of uniaxially loaded sand grains using a novel high-resolution imaging apparatus and ensemble machine learning models

Published:2023-05-28 Issue:5 Volume:17 Page:464-479
ISSN:1938-6362
Container-title:International Journal of Geotechnical Engineering
language:en
Short-container-title:International Journal of Geotechnical Engineering

Author:

Tophel Amir¹^ORCID,Vogt Stefan²,Ramana G. V.³

Affiliation:

1. Department of Civil Engineering, Monash University, Clayton, Australia

2. Research associate, Zentrum Geotechnik, Technische Universität München, München, Germany

3. Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India

Funder

German Academic Exchange Service New Delhi

Publisher

Informa UK Limited

Subject

Soil Science,Geotechnical Engineering and Engineering Geology,Environmental Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/19386362.2023.2264057

Reference80 articles.

1. Abadi, M., P. Barham, J. Chen, et al 2016 Tensorflow: A System for Large-Scale Machine Learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). pp 265–283

2. Stress-History Effects on Shear Modulus of Soils

3. Characterizing Surface Roughness and Shape of Sands Using Digital Microscopy

4. Shear Modulus: A Time-Dependent Soil Property;Anderson D.;Dynamic Geotechnical Testing, ASTM STP 654 American Society for Testing and Materials,1978

5. Experimental Study on the Aging of Sands

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