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Advanced Techniques in Tunnelling
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Advanced Techniques in Tunnelling

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 2431

Special Issue Editor

Dr. Xuewei Liu

E-Mail Website
Guest Editor
State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
Interests: underground engineering; rock failure; rock mechanics; numerical method
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, deeper and longer tunnels have been constructed to meet the needs of transportation, energy storage, mining, and so on. During tunnelling, extreme geological conditions, such as high ground stress, high temperature and soft rock, may easily cause the failure of the surrounding rock. Therefore, many challenges have arisen for tunnel support. Advanced support theory and techniques (including grout material, shotcrete, steel arch, anchor bolt, and the corresponding simulation theory and method) are essential to ensure the safety of a tunnel and have been widely studied. Therefore, in light of the above considerations, we invite investigators to contribute to this Special Issue on “Advance Support Theories and Techniques in Tunnelling” with original research papers. Potential topics include, but are not limited to:

  • Theoretical models for tunnel control;
  • Experimental investigations of mechanical properties of rock mass;
  • Monitoring techniques over the entire life of a tunnel;
  • Novelty cement-based grouting material;
  • Supporting techniques for tunnels;
  • Numerical modelling of rock and structure failure in tunnels;
  • Case studies and other related aspects.

Dr. Xuewei Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • rock mechanics
  • laboratory test
  • numerical simulation
  • theoretical analysis
  • deep rock mass engineering
  • in-field monitoring

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Published Papers (2 papers)

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Research

24 pages, 11993 KiB  
Article
A Method for Extracting Joints on Mountain Tunnel Faces Based on Mask R-CNN Image Segmentation Algorithm
by Honglei Qiao, Xinan Yang, Zuquan Liang, Yu Liu, Zhifan Ge and Jian Zhou
Appl. Sci. 2024, 14(15), 6403; https://doi.org/10.3390/app14156403 - 23 Jul 2024
Viewed by 765
Abstract
The accurate distribution of joints on the tunnel face is crucial for assessing the stability and safety of surrounding rock during tunnel construction. This paper introduces the Mask R-CNN image segmentation algorithm, a state-of-the-art deep learning model, to achieve efficient and accurate identification [...] Read more.
The accurate distribution of joints on the tunnel face is crucial for assessing the stability and safety of surrounding rock during tunnel construction. This paper introduces the Mask R-CNN image segmentation algorithm, a state-of-the-art deep learning model, to achieve efficient and accurate identification and extraction of joints on tunnel face images. First, digital images of tunnel faces were captured and stitched, resulting in 286 complete images suitable for analysis. Then, the joints on the tunnel face were extracted using traditional image processing algorithms, the commonly used U-net image segmentation model, and the Mask R-CNN image segmentation model introduced in this paper to address the lack of recognition accuracy. Finally, the extraction results obtained by the three methods were compared. The comparison results show that the joint extraction method based on the Mask R-CNN image segmentation deep learning model introduced in this paper achieved the best joint extraction effect with a Dice similarity coefficient of 87.48%, outperforming traditional methods and the U-net model, which scored 60.59% and 75.36%, respectively, realizing accurate and efficient acquisition of tunnel face rock joints. These findings suggest that the Mask R-CNN model can be effectively implemented in real-time monitoring systems for tunnel construction projects. Full article
(This article belongs to the Special Issue Advanced Techniques in Tunnelling)
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16 pages, 21265 KiB  
Article
Support Optimization of Open TBM Tunneling in Luohe Formation Sandstone by CT Test and Numerical Simulation
by Xin Kang, Anyun Li, Xiongyao Xie, Kai Zhang, Biao Zhou and Yuanfeng Kang
Appl. Sci. 2023, 13(21), 11812; https://doi.org/10.3390/app132111812 - 29 Oct 2023
Cited by 1 | Viewed by 1135
Abstract
As underground engineering extends into the western and deeper regions of China, more and more Luohe Formation sandstone layers will be encountered, which have weak cementation and high water content. It is a significant challenge to apply the open TBM, and the support [...] Read more.
As underground engineering extends into the western and deeper regions of China, more and more Luohe Formation sandstone layers will be encountered, which have weak cementation and high water content. It is a significant challenge to apply the open TBM, and the support system is crucial in determining whether TBM can excavate quickly and safely. Therefore, in order to optimize the support scheme, this paper analyzes the pore structure and porosity through CT scanning, the results indicate that the volume percentage of pores ≥34 μm is 2.3% and 1.5%, respectively, the large pore apertures are predominant, the surrounding rock has strong permeability, and there is a high risk of rock burst and roof collapse accidents, hence requiring reinforced support measures. On this basis, numerical simulations were conducted to evaluate the support effectiveness. The results show that replacing the “bolt + mesh” with a “bolt + cable + mesh + steel belt”, and replacing the top three bolts with 7.3 m anchor cables, can better control the deformation and provide sufficient thrust force for the TBM, ensuring excavation speed. After the implementation of this scheme at the Kekegai coal mine in Shaanxi, China, the TBM excavation speed increased by 70%, from the previous 10 m/day to 17 m/d, significantly reducing the project duration and construction costs. Full article
(This article belongs to the Special Issue Advanced Techniques in Tunnelling)
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