Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensemble learning methods for capturing the relationships between th...

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Bibliographic Details
Main Authors: Wengang Zhang, Chongzhi Wu, Haiyi Zhong, Yongqin Li, Lin Wang
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:Geoscience Frontiers
Subjects:
Undrained shear strength
Extreme gradient boosting
Random forest
Bayesian optimization
k-fold CV
Online Access:http://www.sciencedirect.com/science/article/pii/S1674987120300669

Internet

http://www.sciencedirect.com/science/article/pii/S1674987120300669

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