Speaker: Hasnain Khan

Date: Friday 6th of November 2026, 1:15pm

Abstract:

Artificial intelligence (AI) and machine learning are increasingly used to model complex
geotechnical and subsurface systems, where the subsurface material behaviour depends on multiple
interacting factors and available experimental data are often limited or heterogeneous. This
presentation will introduce my research at the intersection of AI, data-driven modelling, and
geomechanics, highlighting applications to the prediction and interpretation of soil behaviour and
subsurface properties.
My doctoral research focused on predicting and analyzing the strength development of cement-
treated soils using machine learning and explainable AI approaches. My current postdoctoral
research extends these approaches towards the transfer properties of compacted sand–clay
mixtures, particularly hydraulic conductivity and gas permeability in the context of deep geological
waste disposal. These studies investigate how data-driven models can predict material behaviour
from experimental data, while addressing challenges related to data availability and model
interpretability.
Building on this work, I will discuss future research perspectives involving interpretable and
physically informed machine learning, uncertainty assessment, and the integration of data-driven
approaches with numerical modelling. I will also explore potential synergies between these
directions and the research activities of the RING team and GeoRessources.