A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays-Reference-Cited by-同舟云学术

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A machine learning regression approach for predicting uplift capacity of buried pipelines in anisotropic clays

Published:2023-09 Issue: Volume: Page:100147
ISSN:2667-1433
Container-title:Journal of Pipeline Science and Engineering
language:en
Short-container-title:Journal of Pipeline Science and Engineering

Author:

Lai Van Qui,Kounlavong Khamnoy,Keawsawasvong Suraparb,Bui Truong Son,Huynh Ngoc Thi

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,Mechanical Engineering,Energy (miscellaneous),Fluid Flow and Transfer Processes

Reference43 articles.

1. A comparative study of the effect of the soil constitutive model on the seismic response of buried concrete pipes;Alzabeebee;Journal of Pipeline Science and Engineering,2022

2. Influence of depth thresholds and interaction rules on the burst capacity evaluation of naturally corroded pipelines;Bao;Journal of Pipeline Science and Engineering,2021

3. Experimental and numerical analysis of soil–pipe interaction;Calvetti;J. Geotech. Geoenviron. Eng.,2004

4. The effects of penetration rate and strain softening on the vertical penetration resistance of seabed pipelines;Chatterjee;Géotechnique,2012

5. Numerical simulations of pipe-soil interaction during large lateral movements on clay;Chatterjee;Geotechnique,2012

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Novel hybrid XGBoost-based soft computing models for predicting penetration resistance of buried pipelines in cohesive soils;Ocean Engineering;2024-11

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