Seguir
Fernando Salazar
Fernando Salazar
CIMNE
Dirección de correo verificada de cimne.upc.edu
Título
Citado por
Citado por
Año
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
3262017
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
2242011
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
2192015
Interpretation of dam deformation and leakage with boosted regression trees
F Salazar, MÁ Toledo, E Oñate, B Suárez
Engineering Structures 119, 230-251, 2016
1232016
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
982017
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
982016
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
962017
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
652017
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
402013
A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways
F Salazar, BM Crookston
Water 11 (3), 544, 2019
352019
Coupling machine learning and stochastic finite element to evaluate heterogeneous concrete infrastructure
F Salazar, MA Hariri-Ardebili
Engineering Structures 260, 114190, 2022
332022
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
332021
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
30*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
292020
Anomaly Detection in Dam Behaviour with Machine Learning Classification Models
F Salazar, A Conde, J Irazábal, DJ Vicente
Water 13 (17), 2387, 2021
242021
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
242020
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
20*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
19*2017
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
162019
Air demand estimation in bottom outlets with the particle finite element method-Susqueda Dam case study
F Salazar, J San-Mauro, MÁ Celigueta, E Oñate
Computational Particle Mechanics, 2016
142016
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20