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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree

Singh, Tanvi; Pal, Mahesh; Arora, V. K.

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree Thumbnail


Authors

Tanvi Singh

Mahesh Pal

V. K. Arora



Abstract

M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length (L), angle of oblique load (α), sand density (ρ), number of batter piles (B), and number of vertical piles (V) as input and oblique load (Q) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load (α) and number of batter pile (B) affect the oblique load capacity for both smooth and rough pile groups.

Citation

Singh, T., Pal, M., & Arora, V. K. (2019). Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree. Frontiers of Structural and Civil Engineering, 13(3), 674-685. https://doi.org/10.1007/s11709-018-0505-3

Journal Article Type Article
Acceptance Date Mar 9, 2022
Online Publication Date Aug 30, 2018
Publication Date Jun 1, 2019
Deposit Date Jul 26, 2022
Publicly Available Date Mar 28, 2024
Journal Frontiers of Structural and Civil Engineering
Electronic ISSN 2095-2449
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 13
Issue 3
Pages 674-685
DOI https://doi.org/10.1007/s11709-018-0505-3
Keywords batter piles; oblique load test; neural network; M5 model tree; random forest regression; ANOVA
Public URL https://uwe-repository.worktribe.com/output/9749633
Publisher URL https://link.springer.com/article/10.1007/s11709-018-0505-3#rightslink

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Copyright Statement
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11709-018-0505-3





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