Research

We are equipped with rigorous numerical simulation schemes and advanced imaging techniques in conjunction with well-designed experimental methods to explore how geomaterials behave.

GEMs recently focuses on 'deep learning' for applications in geotechnical engineering. Examples include text-mining of geo-documents, the tunnel mapping, discontinuity extraction, simulation-based estimation of safety, and prediction of stress-strain evolution. 

[Machine learning & Deep-learning based study]

[Text Mining of Geo-Document by deep learning]

[Adaptive and Functional Landfill liner]

[ Waterless stimulation : Fracture propagation]

[Porous block, swelling aggregate]

[Bio-meditated soils]

[Deep learning-based rock classification]

[Hydraulic stimulation: Phase field model]

[Hydraulic stimulation: Imaging]

[Enzyme-Induced Carbonate Precipitation (EICP) and Soil Improvement]

[Lattice-Boltzmann method: Two-phase fluid flow]

[Lattice-Boltzmann Method: Multi-phase fluid flow]

[Deep Learning: crack detection]

[Carbonation in Oil well cement]

[Gas hydrate bearing sediment]

[Micromechanics]

[Engineered soils]

[Lightweight Aggregate and fractures in concrete]

[Particle shape analysis]

[Environmental Construction Materials]