364
Views
10
CrossRef citations to date
0
Altmetric
Original Articles

Acoustic emission monitoring on damage evolution of surrounding rock during headrace tunnel excavation by TBM

, , , , , & show all
Pages 1248-1264 | Received 04 Jan 2017, Accepted 23 May 2017, Published online: 05 Jul 2017
 

Abstract

The acoustic emission (AE) characteristics of the damage evolution of surrounding rock during tunnel-boring machine (TBM) excavation were studied using AE monitoring and ultrasonic testing. The results indicated that the distribution of the AE signals in the surrounding rock were obtained by the reasonable arrangement of the positions of the probes and the multi-parameter filtering method during TBM excavation. For engineering I, rock damage at different degrees along the direction of the TBM advancement was observed within 5 m ahead of the tunnel face during TBM excavation, while the most severe rock damage appeared 1 m ahead of the tunnel face. The difference in the AE events and energy rates helped distinguish the embedding depths of the loose zone, EDZ and disturbance zone, which were 1, 1–3 and 3–8 m from the tunnel wall, respectively. For engineering II, different degrees of rock damage along the axial tunnel direction were observed within 6 m ahead of the tunnel face, with the most severe rock damage occurring 1 m ahead of the tunnel face. The results can provide significant reference values for the safe and efficient application of TBM excavation in engineering processes.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 229.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.