A non-tuned machine learning method to simulate ice-seabed interaction process in clay-Reference-Cited by-同舟云学术

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A non-tuned machine learning method to simulate ice-seabed interaction process in clay

Published:2021-10 Issue: Volume: Page:
ISSN:2667-1433
Container-title:Journal of Pipeline Science and Engineering
language:en
Short-container-title:Journal of Pipeline Science and Engineering

Author:

Azimi Hamed,Shiri Hodjat^ORCID,Malta Eduardo Ribeiro^ORCID

Publisher

Elsevier BV

Reference31 articles.

1. Sensitivity analysis of parameters influencing the ice-seabed interaction in sand by using extreme learning machine;Azimi;Nat. Hazards,2021

2. The influence of non-linear stress-strain behavior of dense sand on seabed response to ice gouging;Nematzadeh;Cold. Reg. Sci. Tech.,2020

3. Evaluation of sea bottom ice scour models (No. 37);Comfort;Environ. Stud. Revolving Funds,1986

4. Note on nearshore ice gouge depths in Alaskan Beaufort Sea;Machemehl;J. Cold Reg. Eng.,1989

5. Evaluation of ice forces acting on sea bed due to ice scouring;Kioka,2000

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