TBM performance prediction using LSTM-based hybrid neural network model: Case study of Baimang River tunnel project in Shenzhen, China-Reference-Cited by-同舟云学术

TBM performance prediction using LSTM-based hybrid neural network model: Case study of Baimang River tunnel project in Shenzhen, China

Published:2023-08 Issue: Volume:11 Page:130-152
ISSN:2467-9674
Container-title:Underground Space
language:en
Short-container-title:Underground Space

Author:

Xu Qihang^ORCID,Huang Xin^ORCID,Zhang Baogang,Zhang Zixin,Wang Junhua,Wang Shuaifeng^ORCID

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Building and Construction,Civil and Structural Engineering

Reference73 articles.

1. Predicting tunnel convergence using multivariate adaptive regression spline and artificial neural network;Adoko;Tunnelling and Underground Space Technology,2013

2. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition;Armaghani;Tunnelling and Underground Space Technology,2017

3. Tunnels in squeezing clay-rich rocks;Arora;Underground Space,2021

4. Boreability predictions for tunneling [Doctoral dissertation, The Norwegian Institute of Technology, Norway];Blindheim,1979

5. Neuro-fuzzy modeling of TBM performance with emphasis on the penetration rate;Bruines;Memoirs of the Centre of Engineering Geology in The Netherlands, Delft,1998

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-Based postural prediction of shield tunneling via machine learning with physical information;Computers and Geotechnics;2024-10

2. Research on Prediction of Excavation Parameters for Deep Buried Tunnel Boring Machine Based on Convolutional Neural Network-Long Short-Term Memory Model;Buildings;2024-08-08

3. A real-time multiple tunneling parameter prediction method of TBM steady phase based on dual recurrent neural networks;Neural Computing and Applications;2024-05-20

4. Prediction of the tunnelling advance speed of a super-large-diameter shield machine based on a KF-CNN-BiGRU hybrid neural network;Measurement;2024-05

5. SSFLNet: A Novel Fault Diagnosis Method for Double Shield TBM Tool System;Sensors;2024-04-20