Session Description:

This session focuses on the role of geotechnical databases in advancing machine learning (ML) and data-driven methods for data-centric geotechnics (e.g., geotechnical site characterization). It aims to highlight how curated datasets can be used for benchmarking, validating, and comparing different models and algorithms. Contributions may include the development of new databases and ML methods, data standardization efforts, case studies using publicly available datasets, and benchmarking frameworks for assessing predictive performance. Discussions on challenges such as data quality, uncertainty, and model generalization are also welcome.