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Machine Learning and Artificial Intelligence in Rock Mechanics

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

Deadline for manuscript submissions: 10 July 2025 | Viewed by 834

Special Issue Editors


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Guest Editor
School of Mechanics and Civil Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: rock mechanics; intelligent mining; lithologic identification
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Energy and Mining Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: open-pit mining; slope engineering; intelligent mining

Special Issue Information

Dear Colleagues,

This Special Issue titled "Machine Learning and Artificial Intelligence in Rock Mechanics" aims to explore the extensive applications of artificial intelligence (AI) and machine learning in rock mechanics and related fields. It covers the latest advancements and innovative applications of AI technology in rock mechanics, geological engineering, mining, underground engineering, and resource development. From data-driven predictive models to intelligent mining and geological explorations, this Special Issue delves into the leveraging of machine learning and AI techniques to enhance the understanding and application of rock mechanics, advancing the frontier of this field. Submissions containing new algorithms, case studies, and technological innovations that provide inspiration and the latest insights to researchers and practitioners of this interdisciplinary field are welcome.

Prof. Dr. Zhongwen Yue
Dr. Hongze Zhao
Guest Editors

Manuscript Submission Information

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Keywords

  • rock mechanics
  • intelligent mining
  • lithologic identification
  • artificial intelligence
  • data-driven models

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Published Papers (1 paper)

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Research

14 pages, 3081 KiB  
Article
Evaluating Rock Brittleness Through a Novel Statistical Damage Theory-Based Index: A Case Study on Sandstones
by Na Wu, Bei Jiang, Ting Ai and Zhengzhao Liang
Appl. Sci. 2024, 14(22), 10153; https://doi.org/10.3390/app142210153 - 6 Nov 2024
Viewed by 377
Abstract
Evaluating the brittleness of rocks or rock masses is a fundamental problem in geotechnical engineering. This study proposed a new index that expresses brittleness as the rate of damage development in rock. The brittleness index was derived from statistical damage theory. It depends [...] Read more.
Evaluating the brittleness of rocks or rock masses is a fundamental problem in geotechnical engineering. This study proposed a new index that expresses brittleness as the rate of damage development in rock. The brittleness index was derived from statistical damage theory. It depends on the four material parameters, i.e., the peak strain, peak strength, Poisson’s ratio and elastic modulus. The validity of the proposed brittleness index was confirmed through two case studies, including triaxial compression test results for coals subjected to varying confining pressures and for sandstones at various temperatures. Uniaxial compression experiments were then performed on rock-like materials to examine the effects of model size and joint dip angle on rock brittleness using the proposed brittleness index. Results show that the brittleness of the jointed specimens varies in a complex pattern with the model size and joint dip angle. Generally, the brittleness index initially reduces and then grows with the increasing joint dip angle, and larger specimens tend to be more brittle. Furthermore, large specimens containing horizontal or vertical joints are particularly susceptible to brittle damage. The proposed brittleness index has merits such as a clear physical meaning and simple expression, making it a valuable tool for evaluating rock brittleness. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Rock Mechanics)
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