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Machine learning-based estimation of soil’s true air-entry value from GSD curves
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Es-haghi, Mohammad Sadegh, Rezania, Mohammad and Bagheri, Meghdad (2023) Machine learning-based estimation of soil’s true air-entry value from GSD curves. Gondwana Research, 123 . pp. 280-292. doi:10.1016/j.gr.2022.06.012 ISSN 1342-937X.
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Official URL: http://dx.doi.org/10.1016/j.gr.2022.06.012
Abstract
The application of machine learning (ML) methods has proven to be promising in dealing with a wide range of geotechnical engineering problems in recent years. ML methods have already been used for the prediction of soil water retention curves (SWRC) and estimation of air-entry values (AEV). However, the reported works in the literature are generally based on limited data and conventional, less accurate approaches for AEV estimation. In this paper, a large database, known as UNsaturated SOil hydraulic DAtabase (UNSODA), is studied and the conventional and true AEVs of 790 soil samples are estimated based on determination methods reported in the literature. A ML approach is then employed for the development of a predictive model for the estimation of true AEV from water content-based SWRCs of a wide range of soil types taking into account the impact of bulk density and grain size distribution parameters. The obtained results reveal an enhanced accuracy in AEV determination, featuring R2 values of 0.964, 0.901 and 0.851 for training, validation, and testing data, respectively, which confirm the high-level performance of the developed ML model. Based on the results of a sensitivity analysis, the particle sizes of 50 and 250 µm are found to have the highest impact on the AEV estimation.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QH Natural history T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Soil mechanics, Hydrologic models, Landscape ecology | ||||||||
Journal or Publication Title: | Gondwana Research | ||||||||
Publisher: | Elsevier B.V. | ||||||||
ISSN: | 1342-937X | ||||||||
Official Date: | November 2023 | ||||||||
Dates: |
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Volume: | 123 | ||||||||
Page Range: | pp. 280-292 | ||||||||
DOI: | 10.1016/j.gr.2022.06.012 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 16 August 2022 | ||||||||
Date of first compliant Open Access: | 24 August 2022 | ||||||||
RIOXX Funder/Project Grant: |
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