DEVELOPMENT OF A SOFTWARE FOR MONITORING AND EVALUATION OF CRITICAL INFRASTRUCTURE SAFETY
PARTNERS: DACARTEC, UPM, CIMNE
The project aims at developing improved techniques for data analysis and control of critical infrastructure safety, with the ultimate aim of:
CIMNE is working on the development and application of machine learning tools to build predicting models. They give an estimate of the performance of the structure in safety conditions, which can be compared with the actual measurements: if the discrepancy exceeds a certain threshold, the system issues a warning to dam safety responsible.
Some of the tools analyzed are neural networks, random forests, support vector machines, boosted regression trees and multi-adaptive regression splines (MARS). The preliminary results support the conclusion that these new models are more flexible and offer greater accuracy than conventional statistical methods.
Currently, the ability of these models to identify patterns of behavior is being assessed, in order to better understand dam behavior.
In parallel, these tools are being integrated into web applications, so that they are fully accessible by the authorized users.