Share This Special Issue

Special Issue Editors

Dr. Jun Zheng

E-Mail Website
Guest Editor

Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
Interests: rock mass structure modeling; artificial intelligence identification of discontinuities; disaster big data analysis and intelligent decision-making

Dr. Mingming He

E-Mail Website
Guest Editor

School of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, China
Interests: unconventional rock mechanical behavior; stability of deep tunnels; geotechnical deep learning and artificial intelligence; digital drilling technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This exclusive collection aims to showcase the latest advancements, cutting-edge research and practical applications of artificial intelligence (AI) in the field of rock mass engineering. The Special Issue will encompass a wide array of topics related to the integration of AI techniques in rock mass engineering, including, but not limited to: (1) AI-based predictive modeling for rock behavior and geomechanical analysis, (2) machine learning algorithms for rock mass classification and characterization, (3) deep learning applications in rock mass deformation and stability analysis, (4) AI-driven optimization and decision-making in rock engineering projects, (5) virtual reality and simulation technologies using AI for rock mass visualization and analysis, (6) application of AI in acquiring and modeling geometric parameters of rock masses, (7) AI-driven rock mass sensing and measurement techniques and (8) data processing and intelligent monitoring systems for sensor data in rock engineering

Dr. Jun Zheng
Dr. Mingming He
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

rock mass
machine learning
discontinuity detection
rock engineering
deep learning
big data
artificial intelligence

Published Papers (1 paper)

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

26 pages, 7890 KiB

Open AccessArticle

Investigation of the Rock-Breaking Mechanism of Drilling under Different Conditions Using Numerical Simulation

by Xinxing Liu, Hao Kou, Xudong Ma and Mingming He

Appl. Sci. 2023, 13(20), 11389; https://doi.org/10.3390/app132011389 - 17 Oct 2023

Viewed by 964

Abstract

The interaction between the drill bit and rock is a complex dynamic problem in the process of drilling and breaking rock. In this paper, the dynamic process of drilling and breaking rock is analyzed using ABAQUS software. The rock-breaking mechanism of drilling is revealed according to the stress–strain state of the rock and the force of the drill bit. The effect of the size of the drill bit and the characteristics of the rock mass on the drilling parameters is studied during the drilling process. The results show that both thrust force and torque show a linear increase with the increasing drilling speed under each fixed rotational speed. The drill bit size has minimal impact on the correlation coefficient of the relationship curves between thrust force, torque, and rotation speed. The drilling results in a soft–hard interlayered rock formation show that there are significant differences in thrust force and torque during the drilling process of different rock types. Whether transitioning from a soft rock layer to a hard rock layer or vice versa, the relationship between thrust force and torque is distinctly manifested whenever there is a change in rock quality. The thrust force and torque increase correspondingly with the increase in confining pressure. When subjected to lateral pressure, thrust force and torque gradually increase with the rising confining pressure. Vertical drilling exhibits a larger increase in thrust force and torque compared to horizontal drilling. The thrust force and torque increase more significantly with the rise in confining pressure compared to lateral pressure. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Rock Mass Engineering)

► Show Figures

Journal Menu

Journal Browser

Application of Artificial Intelligence in Rock Mass Engineering

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (1 paper)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI