Dynamic prediction for attitude and position in shield tunneling based on hybrid deep learning method
In order to reduce the impact of the shield tunneling on the surrounding strata and segments attitude,the shield attitude needs to match the design axis as much as possible to reduce the correction and adjustment of the shield's real-time position.A prediction method based on a hybrid deep learning model was developed,and a dynamic prediction framework for real-time attitude and position in shield tunnel was proposed to predict four key parameters of shield attitude and position.The framework contains Kriging interpolation,wavelet transform(WT)noise filter,convolutional neural networks with channel attention(CNN-CA),and long short-term memory(LSTM)for determining the future attitude and position of the shield.The model performance was validated based on the dataset collected from shield structures in Shanghai metropolitan railroad project,and the contribution of CNN-CA to the model was investigated.Research demonstrates that the model is effective in predicting the attitude of shield,and the CNN-CA can effectively extract the features required for the predicted values,which contributes greatly to computational accuracy.
shield constructionartificial intelligenceattitude and positionhybrid deep learningdynamic prediction
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