Machine Learning and Numerical Modelling in Geotechnical 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 2025 | Viewed by 937
Special Issue Editors
Interests: underground space development; underground mine design; risk assessment in underground projects; ventilation; project cost estimation and feasibility assessment; applications of artificial neural networks in geoengineering
Special Issues, Collections and Topics in MDPI journals
Interests: rock mechanics and rock engineering; tunnelling; stability of underground openings; rock support and reinforcement
Special Issues, Collections and Topics in MDPI journals
Interests: tunnelling; geotechnics; numerical modelling; underground space; sustainability; geothermal; rock mechanics; engineering geology; risk assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The proposed Special Issue of "Machine Learning and Numerical Modelling in Geotechnical Engineering" aims to explore the innovative integration of machine learning (ML) techniques and numerical modelling methodologies to address complex problems in geotechnical engineering. As the field progresses, traditional methods are increasingly complemented by advanced computational tools that offer enhanced predictive capabilities and greater insights into rock/soil behavior, foundation design, tunnels and underground space design, open-pit mine stability, and stability of slopes, as well as other critical aspects of geotechnical engineering.
We invite original research articles, reviews, and case studies that delve into the application of ML algorithms, such as neural networks, support vector machines, deep learning, and advanced/innovative numerical methods to model geotechnical phenomena. Contributions that demonstrate the hybridization of ML with finite element analysis (FEA), discrete element modelling (DEM), and other numerical methods are particularly welcome. We also seek papers that highlight the development and validation of novel ML models using extensive datasets, as well as those that present comparative studies between ML and traditional modelling approaches.
Key topics include, but are not limited to, the following:
- Predictive modelling of soil/rock physical and mechanical properties using ML.
- ML-based optimization in geotechnical design.
- Applications of advanced and innovative 2D/3D/4D numerical modelling in geotechnical engineering.
- Integration/coupling of ML and numerical modelling in geotechnical engineering.
- Case studies demonstrating the practical implementation of ML / numerical modelling in geotechnical projects.
- ML applications in mining operations, including ore body prediction and mine stability analysis.
This Special Issue aims to bridge the gap between theory and practice, offering a comprehensive overview of the current state of the art and future directions in the field. Through this endeavor, we hope to foster interdisciplinary collaboration and innovation, ultimately advancing the knowledge and application of ML and numerical modelling in geotechnical engineering.
Dr. Andreas Benardos
Dr. Pavlos Nomikos
Dr. Chrysothemis Paraskevopoulou
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning in geotechnics
- advanced numerical modelling techniques
- predictive analysis for geotechnical properties
- hybrid ML and numerical methods
- finite element and discrete element coupling
- geotechnical design optimization using ML
- innovative computational geomechanics
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