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An empirical comparison of machine learning techniques for dam behaviour modelling

Author
Salazar, F.; Toledo, M. A.; Oñate, E.; Morán, R.
Type of activity
Journal article
Journal
Structural safety
Date of publication
2015-09
Volume
56
First page
9
Last page
17
DOI
https://doi.org/10.1016/j.strusafe.2015.05.001 Open in new window
Repository
http://hdl.handle.net/2117/76195 Open in new window
URL
http://dx.doi.org/10.1016/j.strusafe.2015.05.001 Open in new window
Abstract
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...
Citation
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.
Keywords
Boosted regression trees, Dam monitoring, Dam safety, Leakage flow, MARS, Machine learning, Neural networks, Random forests, Support vector machines
Group of research
(MC)2 - UPC Computational continuum mechanics
GMNE - Numerical Methods in Engineering Group

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