Autonomous intelligent control of earth pressure balance shield machine based on deep reinforcement learning-Reference-Cited by-同舟云学术

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Autonomous intelligent control of earth pressure balance shield machine based on deep reinforcement learning

Published:2023-10 Issue: Volume:125 Page:106702
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Liu Xuanyu,Zhang Wenshuai,Shao Cheng,Wang Yudong,Cong Qiumei

Funder

National Natural Science Foundation of China

Department of Education of Liaoning Province

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference27 articles.

1. Spatial–temporal fusion network for maximum ground surface settlement prediction during tunnel excavation;Chen;Autom. Constr.,2023

2. Deep reinforcement learning approach to optimize the driving performance of shield tunnelling machines;Elbaz;Tunn. Undergr. Space Technol.,2023

3. Time-series prediction of shield movement performance during tunneling based on hybrid model;Lin;Tunn. Undergr. Space Technol.,2022

4. Determination of earth pressure in earth pressure balance shield construction;Liu;Commod. Qual. Build. Dev.,2014

5. CNN-gru-based intelligent online prediction of screw machine rotation speed for earth pressure balance shield machine;Liu;Eng. Res. Express,2022

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2. Autonomous collaborative optimization control of earth pressure balance shield machine based on hierarchical control architecture;Engineering Applications of Artificial Intelligence;2024-11

3. Real-time inference of compacted soil properties using deflection tests: An AI-driven solution informed by unsaturated soil mechanics principles;Computers and Geotechnics;2024-09

4. A novel 3-DOF active vibration isolation method for TBM’s laser target;Journal of Vibration and Control;2024-01-08

5. A parameter design model for EPB shield soil conditioning in gravel stratum based on machine learning;Construction and Building Materials;2024-01

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