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Interpretation of dam deformation and leakage with boosted regression trees

Autor
Salazar, F.; Toledo, M. A.; Oñate, E.; Suarez, B.
Tipus d'activitat
Article en revista
Revista
Engineering structures
Data de publicació
2016-07
Volum
119
Pàgina inicial
230
Pàgina final
251
DOI
https://doi.org/10.1016/j.engstruct.2016.04.012 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/87614 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0141029616301237 Obrir en finestra nova
Resum
Predictive models are essential in dam safety assessment. They have been traditionally based on simple statistical tools such as the hydrostatic-season-time (HST) model. These tools are well known to have limitations in terms of accuracy and reliability. In the recent years, the examples of application of machine learning and related techniques are becoming more frequent as an alternative to HST. While they proved to feature higher flexibility and prediction accuracy, they are also more difficul...
Citació
Salazar, F., Toledo, M. A., Oñate, E., Suarez, B. Interpretation of dam deformation and leakage with boosted regression trees. "Engineering structures", Juliol 2016, vol. 119, p. 230-251.
Paraules clau
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