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Special Session Proposal for ISMLG 2023

Special Session

Image Analysis and Machine Learning for Geomechanics

 

Session organisers

Budi Zhao, University College Dublin

Eleni Stavropoulou, EPFL (Swiss Federal Institute of Technology Lausanne)

 

Overview

The rising interest and demand for energy geo-structures and energy storage systems require a fundamental understanding of the hydro-chemo-thermo-mechanically coupled processes in soils and rocks for thermal piles, nuclear waste disposal, gas storage and caprock integrity, etc. Advanced imaging techniques provide essential insights into the behaviour of geomaterials under various applied conditions, including hydraulic pressure, chemical reaction, temperature, and mechanical loading. For example, X-ray micro-tomography is a non-destructive 3D imaging tool for the in-situ multi-physical characterisation of geomaterials. Other imaging techniques include digital cameras, neutron tomography, X-ray diffraction, and magnetic resonance imaging.

 

Image analysis and machine learning apply widely in our daily life, from face recognition to self-driving cars. In geomechanics, machine learning is becoming increasingly popular to enhance the quantitative information that image analysis can provide through phase segmentation, fracture recognition, particle tracking, etc. This session aims to attract high-quality contributions that use image analysis and machine learning in geomechanics.

 

The relevant topics include but are not limited to

  • Soil mechanics

  • Rock mechanics

  • Multiphase flow

  • Hydraulic-induced instability

  • Dissolution and precipitation

  • Freezing and thawing

  • Fines migration and clogging

  • Microbially induced calcite precipitation