Paper ID: 10375

Tunnel Settlement Prediction by Transfer Learning

Qicai Zhou1, Hehong Shen1*, Jiong Zhao1& Xiaolei Xiong2

1School of Mechanical Engineering, Tongji University, 4800 Caoan Road, Shanghai, China

2Tongji Zhejiang College, 168 Shangwu Road, Jiaxing, China

*E-mail: shenhhh@foxmail.com

 

 

Abstract

Tunnel settlement has impacts on property security and personal safety significantly. Accurately, tunnel settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point of tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, the source model is constructed and trained by deep learning technology, then parameter-transfer is used to transfer the knowledge gained from the source model to the target model which has a small dataset. After this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the Shanghai metro line 13 tunnel of Jinshajiang Road and proven effective. Artificial neural network and support vector machines were tested as well for comparison, and the results showed that the transfer learning method provided the most accurate tunnel settlement prediction.

Keywords: deep neural network; gated recurrent unit; settlement prediction; tunneling; transfer learning.

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