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Interpretation of dam deformation and leakage with boosted regression trees

Author
Salazar, F.; Toledo, M. A.; Oñate, E.; Suarez, B.
Type of activity
Journal article
Journal
Engineering structures
Date of publication
2016-07
Volume
119
First page
230
Last page
251
DOI
https://doi.org/10.1016/j.engstruct.2016.04.012 Open in new window
Repository
http://hdl.handle.net/2117/87614 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0141029616301237 Open in new window
Abstract
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...
Citation
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.
Keywords
Boosted regression trees, Dam monitoring, Dam safety, Machine learning
Group of research
(MC)2 - UPC Computational continuum mechanics
GMNE - Numerical Methods in Engineering Group
RMEE - Strength of Materials and Structural Engineering Research Group

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