Research
- Machine Learning for health monitoring in dams (PI: Fernando Salazar)
- Development of predictive models based on machine learning and monitoring data for early detection of anomalies and interpretation of dam behavior.
- Numerical modelling of granular materials with DEM. (PI: Joaquin Irazábal)
- Development and application of an implementation of the Discrete Element Model for the analysis of granular geomaterials, with emphasis in railway ballast.
- Operation of water supply networks. (PI: David J. Vicente)
- Development of advanced numerical models of water distribution systems. Water demand prediction and leakage management (including estimation, detection and isolation) by combining statistical analysis, machine learning and numerical modelling.
- Numerical modelling of hydraulic structures. (PI: Javier San Mauro)
- Hydraulic analysis of complex, 3D phenomena such as shockwaves or aeration in spillways and bottom outlets. Non-conventional spillways involving fluid-structure interaction. Development of tailored GUIs
- Efficient uncertainty quantification in dam safety assessment (PIs: Fernando Salazar and Joaquin Irazábal)
- Development of hybrid methodologies, combining machine learning and numerical methods to account for uncertainty in complex settings: non-linear, transient and high dimension problems.
