Fernando Salazar
Fernando Salazar
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Data-based models for the prediction of dam behaviour: a review and some methodological considerations
F Salazar, R Morán, MÁ Toledo, E Oñate
Archives of Computational Methods in Engineering 24 (1), 1-21, 2017
Possibilities of the particle finite element method for fluid–soil–structure interaction problems
E Oñate, MA Celigueta, SR Idelsohn, F Salazar, B Suárez
Computational Mechanics 48 (3), 307-318, 2011
An empirical comparison of machine learning techniques for dam behaviour modelling
RM F Salazar, MÁ Toledo, E Oñate
Structural Safety 56 (doi:10.1016/j.strusafe.2015.05.0), 9-17, 2015
Interpretation of dam deformation and leakage with boosted regression trees
F Salazar, MÁ Toledo, E Oñate, B Suárez
Engineering Structures 119, 230-251, 2016
Numerical modelling of landslide‐generated waves with the particle finite element method (PFEM) and a non‐Newtonian flow model
F Salazar, J Irazábal, A Larese, E Oñate
International Journal for Numerical and Analytical Methods in Geomechanics …, 2016
Early detection of anomalies in dam performance: A methodology based on boosted regression trees
F Salazar, MÁ Toledo, JM González, E Oñate
Structural Control and Health Monitoring 24 (11), e2012, 2017
Numerical modelling of granular materials with spherical discrete particles and the bounded rolling friction model. Application to railway ballast
J Irazábal, F Salazar, E Oñate
Computers and Geotechnics 85, 220-229, 2017
Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach
JM Lezcano-Valverde, F Salazar, L León, E Toledano, JA Jover, ...
Scientific reports 7 (1), 10189, 2017
Analysis of the discharge capacity of radial-gated spillways using CFD and ANN–Oliana Dam case study
F Salazar, R Morán, R Rossi, E Oñate
Journal of Hydraulic Research 51 (3), 244-252, 2013
A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways
F Salazar, BM Crookston
Water 11 (3), 544, 2019
Modelación numérica de deslizamientos de ladera en embalses mediante el método de partículas y elementos finitos (PFEM)
F Salazar, E Oñate, R Morán
Revista Internacional de Métodos Numéricos para Cálculo y Diseño en …, 2012
Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems
MA Hariri-Ardebili, F Salazar
Soft Computing 24 (15), 11583-11604, 2020
Coupling machine learning and stochastic finite element to evaluate heterogeneous concrete infrastructure
F Salazar, MA Hariri-Ardebili
Engineering Structures 260, 114190, 2022
Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction
J Mata, F Salazar, J Barateiro, A Antunes
Water 13 (19), 2717, 2021
Anomaly Detection in Dam Behaviour with Machine Learning Classification Models
F Salazar, A Conde, J Irazábal, DJ Vicente
Water 13 (17), 2387, 2021
Physical and numerical modeling of labyrinth weirs with polyhedral bottom
J San Mauro, F Salazar, MA Toledo, FJ Caballero, C Ponce-Farfan, ...
Ingeniería del Agua 20 (3), 127-138, 2016
A machine learning based methodology for anomaly detection in dam behaviour
F Salazar, E Oñate, MÁ Toledo
PhD Thesis Universitat Politecnica de Catalunya, 2017
A Review on Thermo-mechanical Modelling of Arch Dams During Construction and Operation: Effect of the Reference Temperature on the Stress Field
F Salazar, DJ Vicente, J Irazábal, I De-Pouplana, J San Mauro
Archives of Computational Methods in Engineering 27 (5), 1681-1707, 2020
Physical and numerical modeling for understanding the hydraulic behaviour of Wedge-Shaped-Blocks spillways
FJ Caballero, F Salazar, J San Mauro, MÁ Toledo
Dam Protections against Overtopping and Accidental Leakage, 193, 2015
Effect of the integration scheme on the rotation of non-spherical particles with the discrete element method
J Irazábal, F Salazar, M Santasusana, E Oñate
Computational Particle Mechanics 6 (4), 545-559, 2019
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