Prediction analysis of vertical displacement of pipe jacking crossing existing subway tunnel based on LSTM algorithm

Shuai Tao; Jun-bo Zhang; Xi-bing Han; Jian-qiang Yu; Ling-zhi Xi; Shu-yi Li

doi:10.1117/12.2686338

28 July 2023 Prediction analysis of vertical displacement of pipe jacking crossing existing subway tunnel based on LSTM algorithm

Shuai Tao, Jun-bo Zhang, Xi-bing Han, Jian-qiang Yu, Ling-zhi Xi, Shu-yi Li

Author Affiliations +

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 1275651 (2023) https://doi.org/10.1117/12.2686338
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

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.

Citation Download Citation

Shuai Tao, Jun-bo Zhang, Xi-bing Han, Jian-qiang Yu, Ling-zhi Xi, and Shu-yi Li "Prediction analysis of vertical displacement of pipe jacking crossing existing subway tunnel based on LSTM algorithm", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 1275651 (28 July 2023); https://doi.org/10.1117/12.2686338

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Pipes

Data modeling

Deformation

Deep learning

Education and training

Engineering

Neural networks

Show All Keywords

Keywords/Phrases

Search In:

Publication Years