Welcome to our research group
Welcome to the homepage of the Machine Learning and Computational Geotechnics Group at TU Bergakademie Freiberg. Our group is dedicated to advancing the field of geotechnics by integrating machine learning techniques with computational modeling to solve complex geotechnical and environmental challenges.
Our mission
Our mission is to develop innovative solutions for geotechnical engineering problems through the application of advanced machine learning algorithms and computational methods. We aim to enhance the understanding of subsurface processes and improve the safety, efficiency, and sustainability of geotechnical practices.
Research focus
1. Machine Learning and Physics-Informed Neural Networks (PINNs) in Geotechnics
- Applying machine learning algorithms to predict and model geotechnical properties and behaviors.
- Utilizing PINNs to model and simulate complex hydro-mechanical-chemical processes in geotechnical engineering.
- Integrating machine learning with traditional numerical methods for enhanced modeling accuracy.
2. Computational Modelling
- Numerical simulation of multi-phase flow and transport processes in porous media.
- Hydro-mechanical-chemical coupled processes in geomaterials.
3. Environmental Geotechnics
- Investigating the impact of geotechnical engineering practices on environmental systems.
- Modeling contaminant transport and remediation strategies in soil and groundwater.
- Developing sustainable solutions for waste disposal and resource management in geotechnics.
4. Sustainable Geotechnical Engineering
- Innovations in underground storage technologies for energy and waste management.
- Developing sustainable practices for mining and civil engineering projects.
Our team
- Dr. Reza Taherdangkoo - Group leader
- Team members: Muntasir Shehab, Ruifeng Zhang
Contact us
For information about collaboration opportunities, or to join our team, please contact us at:
Email: reza.taherdangkoo[at]ifgt.tu-freiberg.de