Modernise geotechnical investigation and analysis with machine learning

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The project aims to address the ineffectiveness associated with risk analysis of geotechnical systems by reducing variabilities and by rigorously quantifying such variabilities. It is expected to generate new knowledge in machine-learning-aided risk analysis and in virtual modelling of multiphase-multiphysics-multiscale problems involving random variables. Expected outcomes are datasets and computer tools that are equipped with new functionalities including parameter optimisation, uncertainty quantification, machine-learning based surrogate models and risk analysis. These tools will help to bridge the increasing gap between academic research and engineering practice, transform geo-risk analysis and optimise complex construction processes.