A coupled deep learning approach for shield moving performance prediction of underground tunnel construction-Reference-Cited by-同舟云学术

A coupled deep learning approach for shield moving performance prediction of underground tunnel construction

Published:2023-11 Issue: Volume:123 Page:125-139
ISSN:1342-937X
Container-title:Gondwana Research
language:en
Short-container-title:Gondwana Research

Author:

Lin Song-Shun,Zhou Annan,Shen Shui-Long

Funder

Shantou University

Guangdong Provincial Pearl River Talents Program

Government of Guangdong Province

Publisher

Elsevier BV

Subject

Geology

Reference57 articles.

1. Empirical modeling for predicting excavation performance of EPB TBM based on soil properties;Avunduk;Tunn. Undergr. Space. Technol.,2018

2. Prediction of shield tunneling-induced ground settlement using machine learning techniques;Chen;Front. Struct. Civ. Eng.,2019

3. Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods;Chen;Soils. Found.,2019

4. Investigation of response of metro tunnels due to adjacent large excavation and protective measures in soft soils;Chen;Tunn. Undergr. Space. Technol.,2016

5. Investigation on deformation behavior and failure mechanism of a segmental ring in shield tunnels based on elaborate numerical simulation;Chen;Eng. Fail. Anal,2020

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