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Dynamic prediction for attitude and position in shield tunneling: A deep learning method
پیش بینی پویا برای نگرش و موقعیت در تونل زنی محافظ: یک روش یادگیری عمیق-2019 Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment
erection, the shield driver must constantly adjust the shields attitude and position to reduce a snakelike motion
for fitting the design tunnel axis based on the drivers experience, which is untimely and less reliable during
excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for
the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains
a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory
predictor for determining the attitude and position of the shield machine in the future. The prediction framework
is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang
Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the
feasibility and performance of our method. Results reveal that the proposed model outperforms the other three
similar models in predictive accuracy and provides decision support for adjusting the attitude and position in
shield tunneling. Keywords: Attitude and position | Shield machine | Deep learning | Dynamic prediction | LSTM | Quality managem |
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