Aiming at the development and utilization of underground space in soft soil area, in order to study the influence of pipe jacking construction on the deformation disturbance of existing subway tunnels in soft soil area, the LSTM deep learning algorithm is used to train and verify the LSTM network model, and the settlement deformation of measuring points is predicted. The calculation results show that the variation law of the predicted value is very close to the measured value. The model predicts that the error range of the existing subway tunnel deformation during the pipe jacking construction process can be controlled at the millimeter level, within the allowable error range, and the implementation process is convenient and quick, without too much manual intervention. Under certain conditions, it can replace numerical calculations that are time-consuming and sensitive to grid and constitutive parameters.
Aiming at the development and utilization of underground space in soft soil area, in order to study the influence law of pipe jacking construction on the deformation disturbance of existing subway tunnel in soft soil area, the numerical simulation method combined with field monitoring data and machine learning algorithm was used to simulate and analyze the deformation disturbance law of existing subway tunnel during pipe jacking construction. The results show that the HSS model can better simulate the influence of excavation disturbance on the vertical displacement of the tunnel measuring point. Only when the pipe jacking is out of the hole, the vertical displacement of the measuring point fluctuates greatly, while the deformation of the jacking process is relatively stable. The maximum vertical settlement of the measuring point of the downward tunnel is about 11.3 mm, and the minimum is about 6.81 mm. The maximum vertical displacement of the measuring point of the ascending tunnel is about 17.4mm, and the minimum is about 12.7mm
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.