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Sustainability for Disaster Mitigation in Underground Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1715

Special Issue Editors


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Guest Editor
Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
Interests: digital rock and soil mechanics under extreme environments; nonlinear numerical methods for rock mass; monitoring simulation and digital twin technology for rock mass; disaster prevention and control methods for deep engineering; space rock mechanics and engineering

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Guest Editor
Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
Interests: research on in situ microseismic/acoustic emission monitoring technology and analysis methods for the fracturing process of deep rock masses; study on the mechanisms and control technologies of fracturing disaster initiation processes in deep hard rock engineering
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: rock and soil mechanics; deep underground excavations; tunnelling; environmental geotechnical engineering; mechanical modelling; microbial induced calcium carbonate precipitation (MICP); microstructure failure; analysis; machine learning; damage mechanics; weak interlayer zones
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing scale and complexity of underground engineering projects have amplified the challenges associated with predicting and mitigating geological disasters, which pose significant risks to safety, economic stability, and environmental sustainability. As underground environments become more extreme—characterized by high stress, temperature, and pressure conditions—the demand for innovative solutions is more critical than ever. This Special Issue focuses on developing advanced systems for disaster prediction, early warning, and adaptive control in underground engineering, aiming to minimize the occurrence and impact of hazards such as engineering disasters, earthquakes, landslides, and collapses. By integrating novel technologies like artificial intelligence (AI), digital twins, and IoT-enabled sensor networks, these systems provide real-time monitoring, predictive modeling, and dynamic risk management solutions. These advancements not only enhance the safety and longevity of underground structures but also support sustainable practices by reducing environmental footprints, promoting efficient resource utilization, and ensuring the long-term stability of underground projects in harmony with surrounding ecosystems.

This Special Issue invites cutting-edge research and innovative technological applications related to disaster prediction, early warning, structural stability, and sustainable development in underground engineering. We encourage the submission of original research and review articles focusing on intelligent disaster prevention and control, stability analysis of underground structures, collaborative protection of resources and environments, as well as green materials and low-carbon construction technologies. Emphasis will be placed on how advanced tools such as AI, big data analytics, digital twin technology, and sensor networks can enhance disaster warning and response capabilities while promoting sustainable underground space development.

Topics of interest include, but are not limited to, the following:

  • Advanced predictive modeling and early warning systems for geological hazards in underground engineering, utilizing geological surveys, sensor networks, and big data analytics.
  • Research on the long-term stability of underground structures under extreme conditions (high stress, temperature, and pressure), employing digital geomechanics simulations for dynamic assessments.
  • Collaborative protection of groundwater resources and geological environments, emphasizing sustainable practices to prevent over-exploitation and contamination during underground construction.
  • Adaptive control technologies for managing underground geological hazards, investigating the dynamic response mechanisms of soils and rocks to external disturbances or sudden geological events.
  • Strategies for reusing and restoring abandoned underground spaces, transforming them into functional facilities while mitigating ecological damage.
  • Development of green materials and low-carbon construction techniques to minimize carbon emissions and environmental impact throughout the construction process.
  • Dynamic repair and reinforcement strategies for underground structures post-disaster, ensuring rapid recovery and reducing the risk of secondary disasters.
  • Risk assessment of geological hazards associated with underground energy storage, integrating storage technologies with geomechanical analysis to ensure safety and stability.

We welcome original research articles and reviews on the following areas (and related topics):

  • Integration of AI and machine learning in disaster prediction and management systems for underground engineering.
  • Real-time monitoring systems employing digital twin technology for effective hazard response in underground construction projects.
  • Water-hydrology-mechanics coupled models for optimizing excavation techniques and wastewater treatment to promote sustainable coexistence with water resources.
  • Use of intelligent materials and self-healing technologies to enhance the resilience of underground structures during and after disasters.
  • Case studies demonstrating successful implementation of green engineering practices in underground construction and restoration projects.
  • Evaluation of new materials for low-carbon construction that contribute to the sustainability of underground engineering.

We look forward to your contributions.

Dr. Zhaofeng Wang
Dr. Yaxun Xiao
Prof. Dr. Shuqian Duan
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. Sustainability 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

  • underground engineering
  • geological disasters
  • digital technologies
  • artificial intelligence
  • sustainable development
  • digital twins
  • risk assessment
  • real-time monitoring
  • IoT (Internet of Things)
  • machine learning

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Published Papers (1 paper)

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Research

24 pages, 6038 KiB  
Article
Research on Positioning and Tracking Method of Intelligent Mine Car in Underground Mine Based on YOLOv5 Algorithm and Laser Sensor Fusion
by Linxin Zhang, Xiaoquan Li, Yunjie Sun, Junhong Liu and Yonghe Xu
Sustainability 2025, 17(2), 542; https://doi.org/10.3390/su17020542 - 12 Jan 2025
Cited by 1 | Viewed by 1281
Abstract
Precise positioning has become a key technology in the intelligent development of underground mines. To improve the positioning accuracy of mining vehicles, this paper proposes an intelligent underground mining vehicle positioning and tracking method based on the fusion of the YOLOv5 and laser [...] Read more.
Precise positioning has become a key technology in the intelligent development of underground mines. To improve the positioning accuracy of mining vehicles, this paper proposes an intelligent underground mining vehicle positioning and tracking method based on the fusion of the YOLOv5 and laser sensor technology. The system utilizes a camera and the YOLOv5 algorithm for real-time identification and precise tracking of mining vehicles, while the laser sensor is used to accurately measure the straight-line distance between the vehicle and the positioning device. By combining the strengths of both vision and laser sensors, the system can efficiently identify mining vehicles in complex environments and accurately calculate their position using geometric principles based on laser distance measurements. Experimental results show that the YOLOv5 algorithm can efficiently identify and track mining vehicles in real time. When integrated with the laser sensor’s distance measurement, the system achieves high-precision positioning, with horizontal and vertical positioning errors of 1.66 cm and 1.96 cm, respectively, achieving centimeter-level accuracy overall. This system significantly improves the accuracy and real-time performance of mining vehicle positioning, effectively reducing operational errors and safety risks, providing essential technical support for the intelligent development of underground mining transportation systems. Full article
(This article belongs to the Special Issue Sustainability for Disaster Mitigation in Underground Engineering)
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