EPB machine excavation of mixed soils – Laboratory characterisation-Reference-Cited by-同舟云学术

EPB machine excavation of mixed soils – Laboratory characterisation

Published:2019-08 Issue:4 Volume:12 Page:373-385
ISSN:1865-7362
Container-title:Geomechanics and Tunnelling
language:de
Short-container-title:Geomechanics and Tunnelling

Author:

Oliveira Daniela Garroux G.¹,Thewes Markus²,Diederichs Mark S.³

Affiliation:

1. Herrenknecht AGAbteilung für Forschung und Entwicklung Schlehenweg 2 77963 Schwanau‐Allmannsweier Germany

2. Ruhr Universität BochumLehrstuhl für Tunnelbau, Leitungsbau und Baubetrieb Universitätsstr. 150 44801 Bochum Germany

3. Queen's UniversityDept. of Geological Sciences and Geological Engineering Bruce Wing 36 Union Street 7L 3N6 Kingston ON Canada

Publisher

Wiley

Subject

Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/geot.201900014

Reference31 articles.

1. Project and construction of underground stations and tunnels (TBM and NATM) in heterogeneous masses for Metro do Porto;Fonseca A.;Excavations and tunnels in granite,2010

2. Pressurised TBM tunnelling in mixed face conditions resulting from tropical weathering of igneous rock

Cited by 7 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. Experimental Investigation of the Effects of Aperture Ratio, Chamber Pressure and Soil Conditioning on the Clogging Behavior of EPB Shields;KSCE Journal of Civil Engineering;2023-08-15

2. Evaluation of foam application in providing required conditioning for EPB hard rock TBMs;Geomechanics and Tunnelling;2023-08

3. Experimental study and field validation on soil clogging of EPB shields in completely decomposed granite;Tunnelling and Underground Space Technology;2022-02

4. Pipe Jacking Performance: Mechanistic Behavior and Maintenance Challenges—An Artificial Intelligence-Based Approach;Sustainable Pipe Jacking Technology in the Urban Environment;2022

5. Pipejacking clogging detection in soft alluvial deposits using machine learning algorithms;Tunnelling and Underground Space Technology;2021-07