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Applied Sciences
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Tunnel and Underground Engineering: Recent Advances and Challenges
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Tunnel and Underground Engineering: Recent Advances and Challenges

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 8347

Special Issue Editors

Dr. Zhanping Song

E-Mail Website
Guest Editor
1. School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2. Key Laboratory of Geotechnical and Underground Space Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Interests: tunnel engineering; rock mechanics; intelligent construction; geotechnical engineering
Dr. Yuwei Zhang

E-Mail Website
Guest Editor
School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Interests: metro tunnel; loess; tunnel construction
Dr. Naifei Liu

E-Mail Website
Guest Editor
School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Interests: rock mechanics; tunnel engineering; intelligent construction

Special Issue Information

Dear Colleagues,

Tunnel and underground engineering are the main types of underground space development and utilization, both existing in surrounding rock media. The mechanical problems of surrounding rock media are caused during construction. The mechanical properties of the surrounding rock also affect the construction safety of underground engineering and tunnel engineering. At present, there are still many key technical problems in the construction of tunnel and underground engineering, such as the mechanical evolution mechanism of surrounding rock during the construction of underground engineering and tunnel engineering; the construction safety of underground works and tunnel works in rich water geological environment; and the intelligent construction of underground engineering and tunnel engineering.

This Special Issue aims to collect original research or review articles on the latest theories and techniques related to tunnel and underground engineering. All theoretical, numerical, experimental, and field studies are welcome.

This Special Issue aims to publish high-quality, original research papers in the following overlapping fields (while also not limited to these fields):

  • The mechanical mechanism of tunnel and underground engineering;
  • The informationized monitoring of tunnel and underground engineering;
  • The intelligent development and utilization of urban underground space;
  • The intelligent construction of tunneling engineering;
  • The engineering problems of urban subways;
  • Engineering structure design and optimization;
  • Pipe jacking construction;
  • Rock and soil mechanics with multi-field coupling;
  • A constitutive model of rock and soil;
  • Underground carbon dioxide storage technology;
  • Underground energy storage.

Dr. Zhanping Song
Dr. Yuwei Zhang
Dr. Naifei Liu
Guest Editors

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

  • tunnel engineering
  • underground engineering
  • intelligent construction
  • rock mechanics
  • numerical simulation
  • model tests

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Published Papers (5 papers)

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Research

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19 pages, 10124 KiB  
Article
Size Effect on the Strength Behavior of Cohesionless Soil Under Triaxial Stress State
by Lijia Zhong, Fengyin Liu, Zhonghua Wu, Naifei Liu, Hao Li and Bo Wang
Appl. Sci. 2025, 15(6), 3310; https://doi.org/10.3390/app15063310 - 18 Mar 2025
Viewed by 220
Abstract
Many underground projects are built in cohesionless soil regions, where soil strength is crucial for stability. Particle size greatly influences the mechanical behavior of cohesionless soil. To investigate the relationship between particle size (as a single internal variable) and the strength behavior of [...] Read more.
Many underground projects are built in cohesionless soil regions, where soil strength is crucial for stability. Particle size greatly influences the mechanical behavior of cohesionless soil. To investigate the relationship between particle size (as a single internal variable) and the strength behavior of cohesionless soil, this study employed idealized spherical glass beads of varying sizes as an experimental material. A series of consolidated-drained triaxial compression tests, including both conventional and large-scale tests, were conducted on specimens with different particle sizes. The correlation between particle size and stress-strain behavior, as well as strength characteristics, was analyzed. Additionally, the influence of particle size variations on the macroscopic strength characteristics was investigated. Results indicated that for both small-sized (2 mm–6 mm) or large-sized (10 mm–30 mm) granular materials, the peak shear stress and internal friction angle increased with increased particle size. The strength of large-sized granular materials was significantly higher than that of small-sized ones. During the shear process of large-sized particles, the particle breakage rate initially increased and then decreased with increasing particle size. The internal friction angle rose monotonically with particle size, but showed insensitivity in the 4 mm–5 mm and 20 mm–25 mm particle size ranges. This insensitivity reflects a macroscopic effect resulting from the interplay between the number of inter-particle contacts and the micro-area of their surface, which reaches an extremum. These findings provide valuable insights into the micromechanical interactions governing the strength of behavior of cohesionless soils and highlight the importance considering particle size effects in geotechnical analysis. The derived particle-interaction framework provides theoretical underpinnings for optimizing design methodologies in underground infrastructure projects involving granular media. Full article
(This article belongs to the Special Issue Tunnel and Underground Engineering: Recent Advances and Challenges)
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15 pages, 2670 KiB  
Article
Prediction Model for Cutterhead Rotation Speed Based on Dimensional Analysis and Elastic Net Regression
by Junsheng Liu, Feng Liang, Kai Wei and Changqun Zuo
Appl. Sci. 2025, 15(3), 1298; https://doi.org/10.3390/app15031298 - 27 Jan 2025
Viewed by 761
Abstract
The development and maturation of TBM (tunnel boring machine) technology have significantly improved the accuracy and richness of excavation data, driving advancements in intelligent tunneling research. However, challenges remain in managing data noise and parameter coupling, limiting the interpretability of traditional machine learning [...] Read more.
The development and maturation of TBM (tunnel boring machine) technology have significantly improved the accuracy and richness of excavation data, driving advancements in intelligent tunneling research. However, challenges remain in managing data noise and parameter coupling, limiting the interpretability of traditional machine learning models regarding TBM parameter relationships. This study proposes a cutterhead rotation speed prediction model based on dimensional analysis. By utilizing boxplot methods and low-pass filtering techniques, excavation data were preprocessed to select appropriate operational and mechanical parameters. A dimensionless model was established and integrated with elastic net regression to quantify parameters. Using TBM cluster data from a water diversion tunnel project in Xinjiang, the accuracy and generalizability of the model were validated. Results indicate that the proposed model achieves high prediction accuracy, effectively capturing trends in cutterhead rotation speed while demonstrating strong generalizability. Full article
(This article belongs to the Special Issue Tunnel and Underground Engineering: Recent Advances and Challenges)
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15 pages, 8468 KiB  
Article
Groundwater Discharge Limits of Mountain Tunnels Based on the Normal Growth of Typical Herbaceous Plants
by Yuanfu Zhou, Xuefu Zhang, Yuanpeng Liu and Yuanguang Yang
Appl. Sci. 2024, 14(15), 6561; https://doi.org/10.3390/app14156561 - 26 Jul 2024
Viewed by 1033
Abstract
The construction of mountain tunnels can lead to groundwater loss and severely impact plant growth. In order to study the limited discharge of groundwater in mountain tunnels for the normal growth of typical herbaceous plants, a tunnel in the alpine meadow area of [...] Read more.
The construction of mountain tunnels can lead to groundwater loss and severely impact plant growth. In order to study the limited discharge of groundwater in mountain tunnels for the normal growth of typical herbaceous plants, a tunnel in the alpine meadow area of Qinghai Province was taken as the research objective. Based on transplant experiments, numerical simulations, and the empirical calculation of tunnel discharge limits, the minimum water level required for the normal growth of herbaceous plants, groundwater changes, and grouting parameters during tunnel construction, as well as limited discharge values of groundwater based on the normal growth requirements of plants, were studied. The results indicate that when the groundwater level declined by 0.6–0.8 m, herbaceous plants were able to normally grow. Generally, tunnel excavation lowered the groundwater level so that the normal growth of herbaceous plants was significantly affected. The reasonable grouting parameters were obtained by numerical simulation. They were able to ensure that the groundwater level decline was less than 0.8 m and ultimately recovered to over 90% of the pre-construction level. The herbaceous plants in Qinghai’s alpine grasslands were able to normally grow when the groundwater discharge limit was 0.2~4.0 m3/(m·d). This research offers guidance and support for managing groundwater discharge during tunnel construction in ecologically fragile areas, such as the Three Rivers Source in Qinghai. Full article
(This article belongs to the Special Issue Tunnel and Underground Engineering: Recent Advances and Challenges)
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18 pages, 20014 KiB  
Article
Study on Field Test of Deformation and Stability Control Technology for Shallow Unsymmetrical Loading Section of Super-Large-Span Tunnel Portal
by Li Wan, Yanbin Luo, Changan Zhang, Chaopeng Tian, Xing Shao and Zhen Liu
Appl. Sci. 2024, 14(13), 5796; https://doi.org/10.3390/app14135796 - 2 Jul 2024
Viewed by 1077
Abstract
This study focuses on monitoring the deformation of the shallow unsymmetrical section of a super-large-span tunnel portal relying on the newly built Shimentangshan Tunnel, and through numerical simulations, the construction sequence and drift ratios were optimized to address challenges related to the stability [...] Read more.
This study focuses on monitoring the deformation of the shallow unsymmetrical section of a super-large-span tunnel portal relying on the newly built Shimentangshan Tunnel, and through numerical simulations, the construction sequence and drift ratios were optimized to address challenges related to the stability of surrounding rock and structure. The findings indicate that employing the double-side drift method results in a maximum settlement value of 107.0 mm and a maximum convergence value of 108.8 mm, leading to larger deformations. Excavating the shallow buried side first followed by the deep buried side proves beneficial for deformation control of the support structure and effectively limits damage to the surrounding rock. A drift ratio of 0.3 ensures optimal support structure security and stability. Considering both structural deformation and surrounding rock damage, a ratio between 0.25 and 0.35 for the drifts is recommended. Taking into account construction efficiency and economic benefits, a construction plan for the shallow buried unsymmetrical section at the portal of super-large-span tunnels is proposed. Full article
(This article belongs to the Special Issue Tunnel and Underground Engineering: Recent Advances and Challenges)
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Review

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24 pages, 4395 KiB  
Review
A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
by Chunsheng Su, Qijun Hu, Zifan Yang and Runke Huo
Appl. Sci. 2024, 14(5), 1720; https://doi.org/10.3390/app14051720 - 20 Feb 2024
Cited by 7 | Viewed by 3655
Abstract
With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, [...] Read more.
With the advent of the era of big data and information technology, deep learning (DL) has become a hot trend in the research field of artificial intelligence (AI). The use of deep learning methods for parameter inversion, disease identification, detection, surrounding rock classification, disaster prediction, and other tunnel engineering problems has also become a new trend in recent years, both domestically and internationally. This paper briefly introduces the development process of deep learning. By reviewing a number of published papers on the application of deep learning in tunnel engineering over the past 20 years, this paper discusses the intelligent application of deep learning algorithms in tunnel engineering, including collapse risk assessment, water inrush prediction, crack identification, structural stability evaluation, and seepage erosion in mountain tunnels, urban subway tunnels, and subsea tunnels. Finally, it explores the future challenges and development prospects of deep learning in tunnel engineering. Full article
(This article belongs to the Special Issue Tunnel and Underground Engineering: Recent Advances and Challenges)
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