期刊
GEOSCIENCE FRONTIERS
卷 11, 期 4, 页码 1095-1106出版社
CHINA UNIV GEOSCIENCES, BEIJING
DOI: 10.1016/j.gsf.2019.12.003
关键词
Soft computing method (SCM); Underground excavations; Wall deformation; Predictive capacity
资金
- High-end Foreign Expert Introduction program [G20190022002]
- Chongqing Construction Science and Technology Plan Project [2019-0045]
Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.
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