New computational Tools for RelIability-based dam SafeTy AssessmeNt
Description
TRISTAN develops new computational tools for reliability-based dam safety assessment, combining advanced numerical modelling, finite element methods (FEM), uncertainty analysis, and machine learning approaches. The objective is to improve the prediction of dam behaviour and support more reliable safety evaluations. The project focuses on using machine learning models to support FEM simulations, enabling more efficient prediction of dam response under normal and extreme loading conditions while considering uncertainties and risk.
Organisations
- International Centre for Numerical Methods in Engineering (CIMNE) - Partner
- Universidad Politécnica de Madrid - Partner
Funding
Specific Funding Sources:
N/A
Level of Action:
National: SPAIN
Applications
Large-hydro, Storage Hydropower
Keywords
Civil / Structural engineering, Dams, Digitalization, Hydraulics, Maintenance, Modeling / Simulation, Sustainability
Areas of Research
Research and Innovation Agenda
- Development of advanced reliability-based monitoring and predictive maintenance systems using numerical modelling and machine learning to optimise dam safety assessment and maintenance intervals.
Strategic Industry Roadmap
Last Updated: 30/07/2026 15:59
TRISTAN
Start Date
2019End Date
2021
