Deep Learning in Geomechanics and Geoengineering
DETAILS
Call for Papers
Deep Learning in Geomechanics and Geoengineering
Journal: All Earth
Publisher: Taylor & Francis Group
Article Collection
Manuscript Submission Deadline: 28 May 2027
Open Access: This Article Collection will be published Open Access.
All Earth invites submissions for an Article Collection focused on deep learning methods and their applications in geomechanics and geoengineering, including rock mechanics, underground engineering, geological hazards, data-driven modelling, and intelligent computational approaches.
About the Article Collection
Recent advances in deep learning are creating new opportunities to analyse complex geological and geotechnical systems using experimental, monitoring, imaging, and numerical data. These methods can support feature extraction, pattern recognition, prediction, parameter identification, anomaly detection, and engineering decision-making.
The Collection is particularly interested in how deep learning can complement conventional analytical, experimental, and numerical methods in geomechanics. It also seeks work addressing key challenges such as data quality, interpretability, generalization across geological conditions, multimodal data integration, uncertainty, and the incorporation of physics-based knowledge.
Topics of Interest
Submissions may address, but are not limited to:
Deep learning in geomechanics
Deep learning in geoengineering
Rock and soil characterization
Rock mechanics
Underground engineering
Mechanical behaviour prediction
Geological hazard assessment
Geotechnical monitoring
Infrastructure safety
Damage and failure identification
Experimental data interpretation
Image-based characterization
Acoustic emission analysis
Ultrasonic testing
Digital image correlation
Mechanical parameter prediction
Intelligent interpretation of laboratory measurements
Intelligent interpretation of field measurements
Surrogate modelling
Reduced-order prediction
Large-scale geoengineering simulation
Multi-scale modelling and analysis
Uncertainty quantification
Accelerated numerical computation
Physics-informed learning
Physics-informed neural networks
Multimodal data fusion
Model interpretability
Transfer learning
Generalization across geological conditions
Deep learning for heterogeneous and anisotropic geomaterials
Data-driven modelling of rocks and soils
Deep learning for underground energy storage
Intelligent computational mechanics
Preferred Article Types
The Article Collection particularly welcomes:
Research Articles
Review Articles
All Earth also accepts the following article types more broadly:
Comment
Rapid Communication
Data Note
Method
Registered Report
Research Focus
The Collection emphasizes two closely related research directions.
Deep Learning with Experimental and Diagnostic Techniques
Research may combine deep learning with:
Image-based rock characterization
Acoustic emission
Ultrasonic testing
Digital image correlation
Damage identification
Mechanical parameter estimation
Laboratory measurements
Field monitoring and diagnostics
Deep-Learning-Assisted Modelling and Simulation
Relevant studies may address:
Surrogate models
Reduced-order modelling
Multi-scale analysis
Uncertainty quantification
Accelerated computation
Complex underground systems
Geotechnical systems
Physics-informed AI
Transfer learning
Model interpretability
Multimodal data integration
Keywords
Deep Learning
Geomechanics
Geoengineering
Rock Mechanics
Data-Driven Modeling
Submission Information
Manuscript Submission Deadline: 28 May 2027
All submissions will undergo the journal's standard full peer-review process.
Authors should select Earth Observations from the list of available sections during submission. Selecting the wrong section or Article Collection may delay processing.
Submit an Article to All Earth
For queries regarding discount codes for this Article Collection, authors may contact:
Kara Roberts
Email: kara.roberts@taylorandfrancis.com
Article Collection Guest Advisors
Dr. Renbo Gao
Southwest Jiaotong University
Research interests include rock mechanics, underground engineering, underground energy storage, and deep-learning applications in rock mechanics testing and characterization.
Email: Renbogao116699@163.com
Dr. Cheng Lyu
Southwest Jiaotong University
Research interests include rock mechanics, underground energy storage, geomechanical behaviour of rock masses, and engineering safety.
Email: lyucheng@swjtu.edu.cn
Dr. Zhuhong Wang
Chongqing University
Research interests include AI-driven DIC/DVC, physics-informed neural networks, intelligent computational mechanics, and AI applications in geomechanics and geoengineering.
Email: zh_wang2021@163.com
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