AI implementation on soil element testing (HEIF-warmup 25/26)

Geotechnical engineering relies heavily on soil testing methods such as triaxial, oedometer, shear, and permeability tests. These tests generate critical data used in infrastructure design, but their interpretation can be time-consuming, inconsistent, and dependent on human input. This project seeks to explore whether AI tools such as machine learning and pattern recognition can enhance these processes by accelerating analysis, improving reliability, and enabling predictive modelling. Although AI has shown promise in similar fields, its application to ground testing remains underexplored. This initiative will help lay the foundation for a long-term innovation strategy that bridges the gap between academic research and industry practice in soil testing and geotechnical engineering.