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Applied Sciences
Special Issues
Artificial Intelligence Monitoring and Early Warning in Rock Engineering
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Artificial Intelligence Monitoring and Early Warning in Rock Engineering

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 2597

Special Issue Editors

Dr. Weizhang Liang

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: artificial intelligence; early warning
Prof. Dr. Kang Peng

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: intelligent mining; risk assessment
Dr. Ju Ma

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: monitoring technology; big data
Special Issues, Collections and Topics in MDPI journals
Dr. Hao Wu

E-Mail Website
Guest Editor
School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Interests: risk prediction; rock mechanics

Special Issue Information

Dear Colleagues,

Rock engineering plays an important role in the development of human society. Due to the complexity of engineering geological conditions, some disasters will inevitably occur during the construction process, such as landslide, rockburst, water inrush, large deformation and rock collapse. One of the important reasons lies in the unreliability of data collection and analysis, which makes the workers unable to grasp the spatial and temporal evolution information of disasters in time. Therefore, it is of great significance to study high-precision disaster monitoring technologies and intelligent early-warning approaches. More recently, artificial intelligence (AI) technologies such as machine learning, deep learning, machine vision and intelligent optimization have developed rapidly. They can be adopted to achieve reliable disaster monitoring and early warning in rock engineering. This Special Issue welcomes papers on the state-of-the-art applications of AI in the monitoring and early warning of rock engineering. The key areas include, but are not limited to:

  • Advanced intelligent monitoring technology in rock engineering;
  •  AI in rock fracture signal monitoring;
  • Machine vision in rock deformation monitoring;
  • AI-based dynamic disaster risk assessment;
  • Intelligent diagnosis of disaster precursory information;
  • Time series prediction of monitoring data;
  • Big data in managing disaster information;
  • Early warning methods based on multi-source data.

Dr. Weizhang Liang
Prof. Dr. Kang Peng
Dr. Ju Ma
Dr. Hao Wu
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

  • artificial intelligence (AI)
  • monitoring technology
  • early warning
  • rock engineering
  • risk prediction

Published Papers (2 papers)

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Research

15 pages, 7830 KiB  
Article
Investigation of Microseismic Monitoring of and Precursor Information on Roof Collapse
by Yin Chen, Zeng Chen, Zijun Li and Ping Wang
Appl. Sci. 2023, 13(20), 11307; https://doi.org/10.3390/app132011307 - 14 Oct 2023
Cited by 1 | Viewed by 771
Abstract
Understanding the characteristics and evolution of crack propagation in rock masses is crucial for evaluating their stability. By applying clustering theory to analyze recorded microseismic events, we differentiate the development positions of individual cracks amidst multiple crack formations. Three distinct crack cluster distribution [...] Read more.
Understanding the characteristics and evolution of crack propagation in rock masses is crucial for evaluating their stability. By applying clustering theory to analyze recorded microseismic events, we differentiate the development positions of individual cracks amidst multiple crack formations. Three distinct crack cluster distribution patterns are identified, allowing for the evaluation of regional stability through microseismic event density and ellipsoidal model parameters. The process of crack propagation involves independent development at nucleation positions, mutual influence between adjacent locations, and subsequent crack growth and propagation. Additionally, we examine crack evolution prior to roof collapse and establish a connectivity model between surface and goaf roof cracks. When microseismic events are identified as developing along a plane, it indicates a higher risk of damage in that area. Through the analysis of crack propagation location and angle, our study provides a theoretical foundation for predicting crack direction. Notably, our model’s findings align with onsite observations, demonstrating its practical effectiveness. The results of this research offer valuable insights for collapse prediction and early warning systems for mine roofs, contributing to advancements in mining safety and operations. Full article
(This article belongs to the Special Issue Artificial Intelligence Monitoring and Early Warning in Rock Engineering)
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26 pages, 7971 KiB  
Article
Stress Analysis and Spalling Failure Simulation on Surrounding Rock of Deep Arch Tunnel
by Kang Peng, Guansheng Yi, Song Luo and Xuefeng Si
Appl. Sci. 2023, 13(11), 6474; https://doi.org/10.3390/app13116474 - 25 May 2023
Cited by 6 | Viewed by 1265
Abstract
To study the stress distribution characteristics of surrounding rock and the spalling mechanism of deep hard rock tunnels with different arch heights, the complex variable function and angle-preserving transformation method in elasticity theory were applied to the analytic solution of tangential stress distribution [...] Read more.
To study the stress distribution characteristics of surrounding rock and the spalling mechanism of deep hard rock tunnels with different arch heights, the complex variable function and angle-preserving transformation method in elasticity theory were applied to the analytic solution of tangential stress distribution of arch tunnels during stress adjustment. In addition, true triaxial tests were conducted on granite cube specimens (100 mm × 100 mm × 100 mm) containing holes with three arch heights (including the 25 mm semi-circular arch, 16.7 mm three-centered arch, 12.5 mm three-centered arch) to simulate the spalling process under different initial ground stresses. The stress distribution solution and experimental results show that the initial failure stress of arch holes is 0.39–0.48 times the uniaxial compressive strength (UCS) of the rock. The initial failure location occurs at the arch foot, where tangential stress maximizes. When the lateral pressure coefficient is in the range of 0.38–0.50, the tangential stress is 3.2–3.5 times the UCS. The rock debris of the hole wall are in thin flake shapes. Symmetrical V-shaped or curved failure zones occurred on hole sidewalls. The stress distribution resolution of the surrounding rock of tunnels with different arch heights shows that with the increasing burial depth, the bearing performance of the semi-circular arch tunnel is optimal. In addition, the maximum tangential stress increases as the height of the arch decreases or the lateral stress increases, making it easier for the initial failure to occur at the foot of the arch. Full article
(This article belongs to the Special Issue Artificial Intelligence Monitoring and Early Warning in Rock Engineering)
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