Nogara, C., Salazar, F., 2025. Damage Detection and Localization on Dams Using Autoencoders. NDT and E International (under review).
Tutivén, C., Moyón, L., & Salazar, F. (2025). A robust approach for anomaly detection using 1D convolutional Siamese neural networks to enhance structural health monitoring in dams. Structural Health Monitoring, 14759217251372614. https://doi.org/10.1177/14759217251372614
López-Chacón, S.R.; Salazar, F.; Bladé, E. Interpretation of a Machine Learning Model for Short-Term High Streamflow Prediction. Earth 2025, 6, 64. https://doi.org/10.3390/earth6030064
Silva-Cancino, N.; Salazar, F.; Irazábal, J.; Mata, J. Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach. Infrastructures 2025, 10, 158. https://doi.org/10.3390/infrastructures10070158
Irazábal, J., Salazar, F., Silva-Cancino, N., Vicente, D.J., 2025. Detection of outliers in dam monitoring time series with autoencoders. J Civil Struct Health Monit. https://doi.org/10.1007/s13349-025-00910-4
Pascacio, P., Vicente, D.J., Berruti, I., Nahim Granados, S., Oller, I., Polo-López, M.I., Salazar, F., 2025. Toward the development of an ML-driven decision support system for wastewater treatment: A bacterial inactivation prediction approach in solar photochemical processes. Journal of Environmental Management 373, 123537. https://doi.org/10.1016/j.jenvman.2024.123537
López-Chacón, S.R., Salazar, F., Bladé, E., 2025. Hybrid physically based and machine learning model to enhance high streamflow prediction. Hydrological Sciences Journal 70, 311–333. https://doi.org/10.1080/02626667.2024.2426720
Silva-Cancino, N., Salazar, F., Bladé, E., Sanz-Ramos, M. Influence of breach parameter models on hazard classification of off-stream reservoirs. Water Science and Engineering. 2025, 18(1), pp. 102–114. https://doi.org/10.1016/j.wse.2024.05.001
Pascacio, Pavel, Vicente, D.J., Salazar, F., Guerra-Rodríguez, S., Rodríguez-Chueca, J., 2024. Predictive modeling of Enterococcus sp. removal with limited data from different advanced oxidation processes: A machine learning approach. Journal of Environmental Chemical Engineering 12, 112530. https://doi.org/10.1016/j.jece.2024.112530
Silva-Cancino, N., Salazar, F., Bladé, E., 2024. ACROPOLIS: A graphical user interface for classification of risk for off-stream reservoirs using machine learning. SoftwareX 26, 101657. https://doi.org/10.1016/j.softx.2024.101657
Vicente, D.J., Salazar, F., López-Chacón, S.R., Soriano, C., Martin-Vide, J., 2024. Evaluation of different machine learning approaches for predicting high concentration episodes of ground-level ozone: A case study in Catalonia, Spain. Atmospheric Pollution Research 15, 101999. https://doi.org/10.1016/j.apr.2023.101999
Salazar, F., Irazábal, J., Conde, A., 2024. SOLDIER: SOLution for Dam behavior Interpretation and safety Evaluation with boosted Regression trees. SoftwareX 25, 101598. https://doi.org/10.1016/j.softx.2023.101598
Sanz-Ramos, M., Bladé, E., Silva-Cancino, N., Salazar, F., 2024. Avances en Iber para la clasificación de balsas: proyecto ACROPOLIS. Ingeniería del agua 28, 47–63. https://doi.org/10.4995/ia.2024.20609
Silva-Cancino, N., Salazar, F., Bladé, E., Sanz-Ramos, M., 2024. Influence of breach parameter models on hazard classification of off-stream reservoirs. Water Science and Engineering. https://doi.org/10.1016/j.wse.2024.05.001
Irazábal, J., Salazar, F., Vicente, D.J., 2023. A methodology for calibrating parameters in discrete element models based on machine learning surrogates. Comp. Part. Mech. 10, 1031–1047. https://doi.org/10.1007/s40571-022-00550-1
López-Chacón, S.R., Salazar, F., Bladé, E., 2023. Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction. Water 15, 2020. https://doi.org/10.3390/w15112020
Sanz-Ramos, M., Bladé, E., Silva-Cancino, N., Salazar, F., López-Gómez, D., Martínez-Gomariz, E., 2023. A Probabilistic Approach for Off-Stream Reservoir Failure Flood Hazard Assessment. Water 15, 2202. https://doi.org/10.3390/w15122202
Hariri-Ardebili, M.A., Salazar, F., Pourkamali-Anaraki, F., Mazzà, G., Mata, J., 2023. Soft Computing and Machine Learning in Dam Engineering. Water 15, 917. https://doi.org/10.3390/w15050917
Silva-Cancino, N., Salazar, F., Sanz-Ramos, M., Bladé, E., 2022. A Machine Learning-Based Surrogate Model for the Identification of Risk Zones Due to Off-Stream Reservoir Failure. Water 14, 2416. https://doi.org/10.3390/w14152416
Salazar, F., Hariri-Ardebili, M.A., 2022. Coupling machine learning and stochastic finite element to evaluate heterogeneous concrete infrastructure. Engineering Structures 260, 114190. https://doi.org/10.1016/j.engstruct.2022.114190
Mata, J., Salazar, F., Barateiro, J., & Antunes, A. (2021). Validation of Machine Learning Models for Structural Dam Behaviour Interpretation and Prediction. Water, 13(19), 2717. https://doi.org/10.3390/w13192717
Salazar, F., Conde, A., Irazábal, J., & Vicente, D. J. (2021). Anomaly Detection in Dam Behaviour with Machine Learning Classification Models. Water, 13(17), 2387. https://doi.org/10.3390/w13172387
Hariri-Ardebili, M. A., & Salazar, F. (2019). Engaging soft computing in material and modeling uncertainty quantification of dam engineering problems. Soft Computing. 2020, 24(15), pp. 11583–11604. https://doi.org/10.1007/s00500-019-04623-x
Salazar, F., et al. A review on thermo-mechanical modelling of arch dams during construction and operation: effect of the reference temperature on the stress field. Arch Comp Methods Eng 27.5 (2020): 1681-1707. https://doi.org/10.1007/s11831-020-09439-9
Salazar, F., San-Mauro, J., Celigueta, M. Á., & Oñate, E. (2020). Shockwaves in spillways with the particle finite element method. Computational Particle Mechanics, 7(1), 87-99. https://doi.org/10.1007/s40571-019-00252-1
Irazábal, J., Salazar, F., Santasusana, M., & Oñate, E. (2019). Effect of the integration scheme on the rotation of non-spherical particles with the discrete element method. Computational Particle Mechanics, 6, 545-559. https://doi.org/10.1007/s40571-019-00232-5
Salazar, Fernando, & Crookston, B. M. (2019). A Performance Comparison of Machine Learning Algorithms for Arced Labyrinth Spillways. Water, 11(3), 544. https://doi.org/10.3390/w11030544
San Mauro, J., Toledo, M. Á., Salazar, F., & Caballero, F. J. (2019). A methodology for the design of dam spillways with wedge shaped blocks based on numerical modeling. Revista internacional de métodos numéricos para cálculo y diseño en ingeniería, 35(1). https://doi.org/10.23967/j.rimni.2018.11.001
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Salazar, F., Morán, R., Toledo, M. Á., & Oñate, E. (2017). Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations. Arch Comp Methods Eng, 24(1), 1-21. https://doi.org/10.1007/s11831-015-9157-9
Salazar, F., Toledo, M. Á., González, J. M., & Oñate, E. (2017). Early detection of anomalies in dam performance: A methodology based on boosted regression trees. Structural Control and Health Monitoring, 24(11), e2012. https://doi.org/10.1002/stc.2012
Lezcano-Valverde, J. M., Salazar, F., León, L., Toledano, E., Jover, J. A., Fernandez-Gutierrez, B., ... & Rodriguez-Rodriguez, L. (2017). Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach. Scientific reports, 7(1), 10189. https://doi.org/10.1038/s41598-017-10558-w
Salazar, F., San-Mauro, J., Celigueta, M. Á., & Oñate, E. (2017). Air demand estimation in bottom outlets with the particle finite element method: Susqueda Dam case study. Computational Particle Mechanics, 4(3), 345-356. https://doi.org/10.1007/s40571-016-0117-4
Irazábal, J., Salazar, F., & Oñate, E. (2017). Numerical modelling of granular materials with spherical discrete particles and the bounded rolling friction model. Application to railway ballast. Computers and geotechnics, 85, 220-229. https://doi.org/10.1016/j.compgeo.2016.12.034
Vicente, D. J., San Mauro, J., Salazar, F., & Baena, C. M. (2017). An interactive tool for automatic predimensioning and numerical modeling of arch dams. Mathematical Problems in Engineering, 2017(1), 9856938. https://doi.org/10.1155/2017/9856938
Salazar, F., Toledo, M. Á., Oñate, E., & Suárez, B. (2016). Interpretation of dam deformation and leakage with boosted regression trees. Engineering Structures, 119, 230-251. https://doi.org/10.1016/j.engstruct.2016.04.012
Salazar, F., Irazábal, J., Larese, A., & Oñate, E. (2016). Numerical modelling of landslide‐generated waves with the particle finite element method (PFEM) and a non‐Newtonian flow model. International Journal for Numerical and Analytical Methods in Geomechanics, 40(6), 809-826. https://doi.org/10.1002/nag.2428
Salazar, F., Toledo, M. A., Oñate, E., & Morán, R. (2015). An empirical comparison of machine learning techniques for dam behaviour modelling. Structural Safety, 56, 9-17. https://doi.org/10.1016/j.strusafe.2015.05.001
Pozo, D., Salazar, F., & Toledo, M. Á. (2014). Modelación del funcionamiento hidráulico de los dispositivos de aireación de desagües de fondo de presas mediante el método de partículas y elementos finitos. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 30(1), 51-59. https://doi.org/10.1016/j.rimni.2012.11.002
Salazar, F., Morán, R., Rossi, R., & Oñate, E. (2013). Analysis of the discharge capacity of radial-gated spillways using CFD and ANN – Oliana Dam case study. Journal of Hydraulic Research, 51(3), 244-252. https://doi.org/10.1080/00221686.2012.755714
Salazar, F., Oñate, E., & Morán, R. (2012). Modelación numérica de deslizamientos de ladera en embalses mediante el método de partículas y elementos finitos (PFEM). Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 28(2), 112-123. https://doi.org/10.1016/j.rimni.2012.03.004
Oñate, E., Celigueta, M. A., Idelsohn, S. R., Salazar, F., & Suárez, B. (2011). Possibilities of the particle finite element method for fluid–soil–structure interaction problems. Computational Mechanics, 48, 307-318. https://doi.org/10.1007/s00466-011-0617-2