A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition-Reference-Cited by-同舟云学术

A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition

Published:2022-02 Issue: Volume:165 Page:108353
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Yu Honggan,Tao Jianfeng,Qin Chengjin,Liu Mingyang,Xiao Dengyu,Sun Hao,Liu Chengliang

Funder

National Key Research and Development Program of China

National Key Research and Development Program of China Stem Cell and Translational Research

Science and Technology Commission of Shanghai Municipality

State Key Laboratory of Mechanical System and Vibration

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference69 articles.

1. Application and outlook of information and intelligence technology for safe and efficient TBM construction;Li;Tunn. Undergr. Space Technol.,2019

2. An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network;Jin;Mech. Syst. Signal Proc.,2022

3. Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground;Rostami;Tunn. Undergr. Space Technol.,2016

4. Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines;Zhang;Math. Probl. Eng.,2020

5. Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm;Liu;Tunn. Undergr. Space Technol.,2020

Cited by 51 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. Intelligent optimization prediction of screw conveyor speed for earth pressure balance shield machine based on complete ensemble empirical mode decomposition of adaptive noise and deep learning;Measurement;2025-01

2. A novel identification technology and real-time classification forecasting model based on hybrid machine learning methods in mixed weathered mudstone-sand-pebble formation;Tunnelling and Underground Space Technology;2024-11

3. Semi-supervised recognition of tunnel surrounding rock discontinuities using drilling jumbo data;Automation in Construction;2024-10

4. A physics-based dimension reduction and modeling method for monitoring data and its application to tunnel engineering;Automation in Construction;2024-09

5. Adaptive VMD and multi-stage stabilized transformer-based long-distance forecasting for multiple shield machine tunneling parameters;Automation in Construction;2024-09