Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison
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Title
Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison
Authors
Keywords
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Journal
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-07-06
DOI
10.1007/s11831-021-09615-5
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- (2017) Zhen-Yu Yin et al. INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
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