The Implementation of a Machine-Learning-Based Model Utilizing Meta-heuristic Algorithms for Predicting Pile Bearing Capacity-Reference-Cited by-同舟云学术

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The Implementation of a Machine-Learning-Based Model Utilizing Meta-heuristic Algorithms for Predicting Pile Bearing Capacity

Published:2024-03-30 Issue: Volume: Page:
ISSN:0971-9555
Container-title:Indian Geotechnical Journal
language:en
Short-container-title:Indian Geotech J

Author:

Cai Liang,Zhu Delong,Xu Kang

Funder

Provincial Teaching and Research Project of Higher Education Institutions in Hubei Province

Education Research Project of the Hubei Higher Education Association

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40098-024-00933-6.pdf

Reference64 articles.

1. Meyerhof GG (1976) Bearing capacity and settlement of pile foundations. J Geotech Eng Div 102(3):197–228

2. Maizir H, Suryanita R (2018) “Evaluation of axial pile bearing capacity based on pile driving analyzer (PDA) test using neural network,” in IOP Conference series: earth and environmental science, IOP publishing, p 12037

3. Jahed Armaghani D, Shoib RSNSBR, Faizi K, Rashid ASA (2017) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl 28:391–405

4. Pham K, Kim D, Park S, Choi H (2021) Ensemble learning-based classification models for slope stability analysis. CATENA 196:104886

5. Shahin MA (2013) “Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions,” Metaheuristics water Geotech Transp Eng, vol 169204

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. Research on pile bearing capacity prediction improved by grey wolf optimization in the SSA-LSSVM model;Measurement Science and Technology;2024-08-29

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