Organised by
The Fifth International Workshop on the Future of Machine Learning in Geotechnics
Bridging AI Innovation and Geotechnical Industry Practice
Conference begins in
World-Class Experts
Featured Speakers
Distinguished researchers and industry leaders shaping the future of machine learning in geotechnics.
Plan Ahead
Important Dates
Scope of Research
Research Themes
FOMLIG 2027 covers the full spectrum of machine learning and AI applications in geotechnical engineering, underground construction, and related disciplines.
Large Language Models & Foundation Models
Applying LLMs and foundation models to geotechnical knowledge extraction and engineering design.
Physics-informed Machine Learning
Integrating physical laws and domain knowledge into ML architectures for reliable geotechnical predictions.
Digital Twins for Geotechnical Structures
Real-time digital replicas of tunnels, mines, and underground infrastructure for monitoring and management.
Explainable AI (XAI)
Interpretable and transparent AI models for geotechnical risk assessment and engineering decision support.
Rock Mechanics & Tunnelling
ML-driven advances in rock mass characterisation, tunnel design, and underground excavation engineering.
Geotechnical Risk Assessment
Probabilistic and AI-based frameworks for quantifying and managing geotechnical hazards and uncertainties.
Smart Construction & Infrastructure
IoT, sensors, and AI for intelligent monitoring and adaptive management of geotechnical infrastructure.
Data Analytics & Visualization
Advanced data processing, statistical learning, and interactive visualization for geotechnical datasets.
Computational Mechanics
High-performance numerical methods, FEM/DEM, and ML-accelerated simulation for geomechanical problems.