Carregant...
Carregant...

Vés al contingut (premeu Retorn)

Early detection of anomalies in dam performance: a methodology based on boosted regression trees

Autor
Salazar, F.; Toledo, M. A.; Gonzalez, J.; Oñate, E.
Tipus d'activitat
Article en revista
Revista
Structural control & health monitoring
Data de publicació
2017-11
Volum
24
Número
11
Pàgina inicial
1
Pàgina final
16
DOI
https://doi.org/10.1002/stc.2012 Obrir en finestra nova
Repositori
https://www.researchgate.net/publication/315983644_Early_detection_of_anomalies_in_dam_performance_A_methodology_based_on_boosted_regression_trees Obrir en finestra nova
URL
http://onlinelibrary.wiley.com/doi/10.1002/stc.2012/abstract;jsessionid=806ED7A76FACCCFBF656B148EF586DB1.f04t01 Obrir en finestra nova
Resum
The advances in information and communication technologies led to a general trend towards the availability of more detailed information on dam behaviour. This allows applying advanced data-based algorithms in its analysis, which has been reflected in an increasing interest in the field. However, most of the related literature is limited to the evaluation of model prediction accuracy, whereas the ulterior objective of data analysis is dam safety assessment. In this work, a machine-learning algori...
Paraules clau
anomaly detection, boosted regression trees, dam monitoring, dam safety, machine learning
Grup de recerca
(MC)2 - UPC Mecànica de Medis Continus i Computacional
DECA - Grup de Recerca del Departament d'Enginyeria Civil i Ambiental
GMNE - Grup de Mètodes Numèrics en Enginyeria

Participants