High-Efficiency TBM Tunnelling and Intelligent Control in Deep and Complex Geological Formations
DETAILS
Call for Papers
High-Efficiency TBM Tunnelling and Intelligent Control in Deep and Complex Geological Formations
Journal: Deep Resources Engineering
Publisher: Elsevier
Special Issue
Call for Papers Published: 8 September 2026
Manuscript Submission Deadline: 28 February 2027
Open Access: Yes
Article Processing Charge (APC): No APC required
Deep Resources Engineering invites submissions for a Special Issue focused on high-efficiency Tunnel Boring Machine (TBM) tunnelling and intelligent control under deep, extreme, and complex geological conditions.
About the Special Issue
As underground engineering moves toward kilometre-scale depths and increasingly challenging geological environments, TBM operations face conditions such as high in-situ stress, elevated ground temperatures, high gas pressure, alternating soft and hard rock, and fault-fracture zones. These conditions can reduce rock-breaking efficiency and increase risks such as rockbursts, large deformation, TBM jamming, and abnormal cutter damage.
The Special Issue explores how artificial intelligence, big data, digital twins, advanced sensing, autonomous decision-making, and adaptive control can transform conventional experience-driven TBM construction. It seeks research spanning the complete intelligent tunnelling chain—from geological perception and parameter optimization to autonomous excavation, hazard management, equipment design, and life-cycle operation and maintenance.
Topics of Interest
Submissions may address, but are not limited to:
Fine-scale characterization of deep geological formations
Advanced geological prediction
Real-time geological sensing and information acquisition
Intelligent TBM control
Autonomous TBM tunnelling
Self-optimization of tunnelling parameters
AI-driven tunnelling decision-making
Adaptive TBM control
Rock-machine interaction mechanisms
Rock-breaking mechanisms under deep underground conditions
Improvement of rock-breaking efficiency
High in-situ stress environments
High-temperature underground environments
High gas-pressure conditions
Interbedded soft and hard rock
Fault and fracture zones
Rockburst mechanisms and prediction
Large deformation hazards
TBM jamming prediction and prevention
Cutter damage and intelligent monitoring
Intelligent hazard prevention and control
AI and big data for underground engineering
Digital twins for TBM tunnelling
Data-driven tunnelling systems
Intelligent perception and autonomous decision-making
Adaptability-oriented TBM design
Equipment reliability in extreme geological conditions
TBM operation and maintenance
Life-cycle management of TBM equipment
Generalization of intelligent control models
Preferred Article Types
The Special Issue welcomes:
Original Research Papers
State-of-the-Art Reviews
Commentary
Correspondence
Short Communications
Research Focus
The Special Issue particularly focuses on five interconnected areas:
1. Geological Characterization and Prediction
Fine-scale characterization and advanced prediction methods for deep and complex geological formations.
2. Intelligent and Autonomous Tunnelling
AI-enabled control, autonomous excavation, tunnelling parameter optimization, and adaptive decision-making.
3. Rock–Machine Interaction
Understanding rock-machine interactions and improving rock-breaking efficiency under deep underground conditions.
4. Intelligent Hazard Management
Mechanisms, prediction, monitoring, prevention, and intelligent control of hazards associated with deep TBM tunnelling.
5. TBM Design and Life-Cycle Management
Adaptability-oriented equipment design, operation, maintenance, and life-cycle management for challenging geological environments.
Submission Information
Manuscript Submission Deadline: 28 February 2027
Manuscripts must be original and should not have been previously published or be simultaneously under consideration elsewhere, except for conference proceedings papers as specified by the journal.
All submissions will undergo a single-blind peer-review process.
No Article Processing Charge (APC) is required for articles.
Submit a Manuscript to Deep Resources Engineering
Deep Resources Engineering – Guide for Authors
Deep Resources Engineering – Journal Page
For Special Issue questions, authors may also contact the editorial office at deepre@mail.neu.edu.cn.
Guest Editors
Wei Wu
Associate Professor
Nanyang Technological University
Bingqian Yan
Associate Professor
University of Science and Technology Beijing
Wenbo Zheng
Associate Professor
University of Northern British Columbia
Zhenliang Zhou
Associate Professor
Beijing Jiaotong University
Posted on ServiceSetu Academics — Premier Platform for Academic Opportunities & Research Collaboration.
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