Pathway to a fully data-driven geotechnics: lessons from materials
  informatics
Stephen Wu Yu Otake Yosuke Higo Ikumasa Yoshida
This paper elucidates the challenges and opportunities inherent in
integrating data-driven methodologies into geotechnics, drawing inspiration
from the success of materials informatics. Highlighting the intricacies of soil
complexity, heterogeneity, and the lack of comprehensive data, the discussion
underscores the pressing need for community-driven database initiatives and
open science movements. By leveraging the transformative power of deep
learning, particularly in feature extraction from high-dimensional data and the
potential of transfer learning, we envision a paradigm shift towards a more
collaborative and innovative geotechnics field. The paper concludes with a
forward-looking stance, emphasizing the revolutionary potential brought about
by advanced computational tools like large language models in reshaping
geotechnics informatics.