《结构与土木工程前沿（英文）》
>> 2019年
第13卷
第3期
doi:
10.1007/s11709-018-0505-3
RESEARCH ARTICLE
Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression and M5 model tree
Department of Civil Engineering, National Institute of Technology, Kurukshetra, Haryana 136119, India
录用日期：
2018-08-23
发布日期：
2018-08-23
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摘要
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 ( ), angle of oblique load ( ), sand density ( ), number of batter piles ( ), and number of vertical piles ( ) as input and oblique load ( ) 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 ( ) and number of batter pile ( ) affect the oblique load capacity for both smooth and rough pile groups.
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