Prediction of pile-bearing capacity using Least Square Support Vector Regression: individual and hybrid models development-Reference-Cited by-同舟云学术

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Prediction of pile-bearing capacity using Least Square Support Vector Regression: individual and hybrid models development

Published:2024-02-06 Issue:3 Volume:7 Page:2701-2715
ISSN:2520-8160
Container-title:Multiscale and Multidisciplinary Modeling, Experiments and Design
language:en
Short-container-title:Multiscale and Multidiscip. Model. Exp. and Des.

Author:

Yang Xiaochuan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s41939-023-00357-4.pdf

Reference55 articles.

1. Abualigah L et al (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

2. Abu-Farsakh MY, Titi HH (2004) Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles. J Geotech Geoenviron Eng 130(9):935–944

3. Adeli H (2001) Neural networks in civil engineering: 1989–2000. Comput-Aided Civ Infrastruct Eng 16(2):126–142

4. Aiyer BG et al (2014) Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine. KSCE J Civ Eng 18(6):1753–1758

5. Akbarzadeh MR et al (2023) Estimating compressive strength of concrete using neural electromagnetic field optimization. Materials 16(11):4200

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. An Evolutionary Polynomial Computing of Pile Capacity Using the Results of High-strain Dynamic Test;Transportation Infrastructure Geotechnology;2024-05-11

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