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An empirical comparison of machine learning techniques for dam behaviour modelling

Autor
Salazar, F.; Toledo, M. A.; Oñate, E.; Morán, R.
Tipus d'activitat
Article en revista
Revista
Structural safety
Data de publicació
2015-09
Volum
56
Pàgina inicial
9
Pàgina final
17
DOI
https://doi.org/10.1016/j.strusafe.2015.05.001 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/76195 Obrir en finestra nova
URL
http://dx.doi.org/10.1016/j.strusafe.2015.05.001 Obrir en finestra nova
Resum
Predictive models are essential in dam safety assessment. Both deterministic and statistical models applied in the day-to-day practice have demonstrated to be useful, although they show relevant limitations at the same time. On another note, powerful learning algorithms have been developed in the field of machine learning (ML), which have been applied to solve practical problems. The work aims at testing the prediction capability of some state-of-the-art algorithms to model dam behaviour, in ter...
Citació
Salazar, F., Toledo, M. A., Oñate, E., Morán, R. An empirical comparison of machine learning techniques for dam behaviour modelling. "Structural safety", Setembre 2015, p. 9-17.
Paraules clau
Boosted regression trees, Dam monitoring, Dam safety, Leakage flow, MARS, Machine learning, Neural networks, Random forests, Support vector machines
Grup de recerca
(MC)2 - UPC Mecànica de Medis Continus i Computacional
GMNE - Grup de Mètodes Numèrics en Enginyeria

Participants

Arxius