Machine Learning for Resilient Geoinfrastructure

The session will cover a range of topics in geotechnical engineering, including benchmarking datasets, sensors and field studies, as well as the quantification of uncertainty, heterogeneity and nonlinearity. It will explore the use of generative AI and physics-informed neural networks in geotechnics, with applications to geo-infrastructures and geo-hazards. Additionally, the session will discuss explainable AI for geotechnics. The event will also feature the prize ceremony for the GRID Data Science Competition, which engages young researchers and students in developing machine learning models using a GRID dataset and will recognize their innovative contributions to Geo-Engineering.