典型文献
Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement
文献摘要：
The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engi-neering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model's robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress Eur/σ'V,the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.
文献关键词：
中图分类号：
作者姓名：
Dongmei Zhang;Yiming Shen;Zhongkai Huang;Xiaochuang Xie
作者机构：
Key Laboratory of Geotechnical and Underground Engineering.Ministry of Education,Tongji University,Shanghai.200092,China;Department of Geotechnical Engineering,College of Civil Engineering,Tongji University,Shanghai,200092,China
文献出处：
引用格式：
[1]Dongmei Zhang;Yiming Shen;Zhongkai Huang;Xiaochuang Xie-.Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement)[J].岩石力学与岩土工程学报（英文版）,2022(04):1100-1114
A类：
B类：
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AB值：
0.592376
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