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- Title
基于梯度提升决策树算法的膨润土膨胀力预测.
- Authors
王琼; 张佳南; 高岑; 苏薇; 刘樟荣; 叶为民
- Abstract
Bentonite is the preferred buffer/backfill barrier material for deep geological disposal repository due to its high swelling ability,low permeability and high nuclide adsorption capacity. In actual engineering, the swelling pressure of highly compacted bentonite is one of the key indexes for buffer/backfill barrier design. Previous studies showed that there are many factors affecting the swelling pressure of bentonite and the mechanisms are complex,it is difficult for the existing theorical models to analyze the influence of multi-factors comprehensively. Therefore, one of the most effective machine learning algorithms,Gradient Boosting Decision Tree (GBDT), was used in this study to comprehensively analyze the influence of different parameters and predict the swelling pressure. Compared with other machine learning algorithms,the GBDT model showed much higher precision with a value of 91.7 % . The GBDT regression model is expected to provide technical parameters and theoretical support for the field test of underground laboratory and the construction of disposal repository built in China.
- Subjects
CHINA; GEOLOGICAL repositories; UNDERGROUND construction; MACHINE learning; ADSORPTION capacity; BENTONITE
- Publication
World Nuclear Geoscience, 2023, Vol 40, Issue 3, p775
- ISSN
1672-0636
- Publication type
Article
- DOI
10.3969/j.issn.1672-0636.2023.03.008