Application of artificial neural networks for predicting lateral and uplift capacity of buried rectangular box carrying pipelines-Reference-Cited by-同舟云学术

Application of artificial neural networks for predicting lateral and uplift capacity of buried rectangular box carrying pipelines

Published:2024-01-05 Issue: Volume: Page:1-17
ISSN:1064-119X
Container-title:Marine Georesources & Geotechnology
language:en
Short-container-title:Marine Georesources & Geotechnology

Author:

Kounlavong Khamnoy¹,Lai Van Qui²³^ORCID,Chavda Jitesh T.⁴^ORCID,Banyong Rungkhun¹,Jamsawang Pitthaya⁵,Keawsawasvong Suraparb¹^ORCID

Affiliation:

1. Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand

2. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam

3. Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Vietnam

4. Assistant Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

5. Soil Engineering Research Center, Department of Civil Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

Funder

National Science, Research and Innovation Fund

King Mongkut’s University of Technology North Bangkok

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/1064119X.2023.2300046

Reference61 articles.

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2. Ali J. I. 1968. Pull-out Resistance of Anchor Plates and Anchored Piles in Soft Bentonite Clay. MSc thesis NC: Duke University.

3. Pullout capacity of strip plate anchor in cohesive sloping ground under undrained condition

4. Improvement in uplift capacity of horizontal circular anchor plate in undrained clay by granular column

5. Pullout Capacity of Shallow Inclined Anchor in Anisotropic and Nonhomogeneous Undrained Clay;Bhattacharya P.;Geomechanics and Engineering,2017

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