Quantitative evaluation of service health condition for cutting tools on cutterhead in long-distance mechanized shield tunneling-Reference-Cited by-同舟云学术

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Quantitative evaluation of service health condition for cutting tools on cutterhead in long-distance mechanized shield tunneling

Published:2023-07 Issue: Volume:137 Page:105115
ISSN:0886-7798
Container-title:Tunnelling and Underground Space Technology
language:en
Short-container-title:Tunnelling and Underground Space Technology

Author:

Wei Yingjie,Yang Yuyou,Tao Mingjiang,Qiu Tong,Jie Yuxin

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

State Key Laboratory of Hydroscience and Engineering

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Building and Construction

Reference31 articles.

1. Evaluation of Granular Soil Abrasivity for Wear on Cutting Tools in Excavation and Tunneling Equipment;Alavi Gharahbagh;J. Geotech. Geoenviron. Eng.,2013

2. Evaluation of tool wear in EPB tunneling of Tehran Metro, Line 7 Expansion;Amoun;Tunn. Undergr. Space Technol.,2017

3. EPB tunneling challenges in bouldery ground: a new experience on the Tabriz metro line 1, Iran;Barzegari;Bull. Eng. Geol. Environ.,2014

4. Parametric study of soil abrasivity for predicting wear issue in TBM tunneling projects;Barzegari;Tunnel. Undergr. Space Technol.,2015

5. Challenges and technological breakthroughs in tunnel construction in China;Chen;China J. Highway Transport,2020

Cited by 5 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. Active learning-based research of foaming agent for EPB shield soil conditioning in gravel stratum;Measurement;2025-01

2. Identification of cutter flat wear for rock TBMs using vibration signal of shield;Mechanical Systems and Signal Processing;2025-01

3. 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

4. An innovative multisource multibranch metric ensemble deep transfer learning algorithm for tool wear monitoring;Advanced Engineering Informatics;2024-10

5. Evaluation of fine material and chip formation in rock cutting with a conical tool;Bulletin of Engineering Geology and the Environment;2024-06-13

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