Definition of warning thresholds for dam safety using artificial intelligence and a non-euclidean affinity assessment metric
Description
The AlDA project aimed to improve dam safety through the application of artificial intelligence techniques and the development of a non-Euclidean affinity metric to identify similarities between dams. Recognising the need for more objective methods in dam safety management, the project addressed the limitations of traditional approaches for defining emergency warning thresholds, which typically rely on simplified statistical analyses and expert judgement. By applying advanced AI methods—including neural networks, random forests, Bayesian networks, and other machine learning techniques—the project sought to develop objective methodologies for defining emergency thresholds based on dam monitoring data. In addition, it developed methods to estimate reference behavioural models for newly commissioned dams by analysing data from similar existing dams, thereby improving safety assessments during the critical early stages of operation when historical monitoring data are limited. The project combined expertise in computational modelling from CIMNE with dam engineering expertise from the Universidad Politécnica de Madrid (UPM) to develop innovative tools for risk-informed dam safety assessment and emergency planning.
Organisations
- International Centre for Numerical Methods in Engineering (CIMNE) - Partner
- Universidad Politécnica de Madrid - Partner
Funding
- National funding
Specific Funding Sources:
Ministerio de Economía y Competitividad
Level of Action:
National: SPAIN
Applications
Large-hydro, Storage Hydropower
Keywords
Civil / Structural engineering, Dams, Digitalization, Maintenance, Modeling / Simulation
Areas of Research
Research and Innovation Agenda
- Development of artificial intelligence-based methods for defining objective emergency warning thresholds and predictive monitoring systems to improve dam safety assessment and maintenance planning.
Strategic Industry Roadmap
Last Updated: 30/07/2026 15:57
