Publicacións

@article{farfan-duran_surrogate-assisted_2024,

title = {Surrogate-Assisted Evolutionary Algorithm for the Calibration of Distributed Hydrological Models Based on Two-Dimensional Shallow Water Equations},

author = {Juan F. Farfán-Durán and Arash Heidari and Tom Dhaene and Ivo Couckuyt and Luis Cea-Gómez},

url = {https://www.mdpi.com/2073-4441/16/5/652},

doi = {10.3390/w16050652},

issn = {2073-4441},

year  = {2024},

date = {2024-01-01},

urldate = {2026-04-20},

journal = {Water},

volume = {16},

number = {5},

pages = {652},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Distributed hydrological models based on shallow water equations have gained popularity in recent years for the simulation of storm events, due to their robust and physically based routing of surface runoff through the whole catchment, including hill slopes and water streams. However, significant challenges arise in their calibration due to their relatively high computational cost and the extensive parameter space. This study presents a surrogate-assisted evolutionary algorithm (SA-EA) for the calibration of a distributed hydrological model based on 2D shallow water equations. A surrogate model is used to reduce the computational cost of the calibration process by creating a simulation of the solution space, while an evolutionary algorithm guides the search for suitable parameter sets within the simulated space. The proposed methodology is evaluated in four rainfall events located in the northwest of Spain: one synthetic storm and three real storms in the Mandeo River basin. The results show that the SA-EA accelerates convergence and obtains superior fit values when compared to a conventional global calibration technique, reducing the execution time by up to six times and achieving between 98% and 100% accuracy in identifying behavioral parameter sets after four generations of the SA-EA. The proposed methodology offers an efficient solution for the calibration of complex hydrological models, delivering improved computational efficiency and robust performance.},

keywords = {evolutionary algorithm, hydrological model, Optimization, shallow water equations, Surrogate model},

pubstate = {published},

tppubtype = {article}

}

@article{cid_probability-damage_2020,

title = {Probability-damage approach for fail-safe design optimization (PDFSO)},

author = {Clara Cid and Aitor Baldomir-García and Santiago Hernández-Ibáñez},

url = {https://doi.org/10.1007/s00158-020-02660-x},

doi = {10.1007/s00158-020-02660-x},

issn = {1615-1488},

year  = {2020},

date = {2020-12-01},

urldate = {2026-08-06},

journal = {Structural and Multidisciplinary Optimization},

volume = {62},

number = {6},

pages = {3149–3163},

abstract = {Fail-safe design often leads to oversized structures as the design must be able to behave properly for the different accidental situations that could undergo throughout their lifespan. The existing research about structural optimization applied to fail-safe design does not contemplate the idea of having accidental situations with different probabilities of occurrence. This means that the structure must satisfy the design constraints in the damaged structure regardless of whether one accidental situation is much more likely than another. The objective of this research is to formulate a new optimization strategy to get fail-safe structures with minimum weight taking into account the available data about the probability of occurrence associated to each partial collapse. A multi-model probabilistic optimization problem is defined and applied to two structures: a 2D truss structure with stress constraints as well as the tail section of an aircraft fuselage with stress and buckling constraints.},

keywords = {Certainty of survival, Fail-safe design, Multi-model, Optimization, Partial collapse, Probability of failure, Uncertainty},

pubstate = {published},

tppubtype = {article}

}

@article{kusano_importance_2018,

title = {The importance of correlation among flutter derivatives for the reliability based optimum design of suspension bridges},

author = {Ibuki Kusano and Aitor Baldomir-García and José Ángel Jurado-Albarracín-Martinón and Santiago Hernández-Ibáñez},

url = {https://www.sciencedirect.com/science/article/pii/S014102961733523X},

doi = {10.1016/j.engstruct.2018.06.091},

issn = {0141-0296},

year  = {2018},

date = {2018-10-01},

urldate = {2026-08-05},

journal = {Engineering Structures},

volume = {173},

pages = {416–428},

abstract = {The design of long-span bridges is constrained by the uncertainty in the evaluation of flutter velocity. Among all the elements that take part in the flutter assessment, the uncertainty level in experimentally obtained flutter derivatives has the most impact. It is therefore important in the evaluation of flutter velocity to assess the uncertainty which is associated with the adopted experimental method for flutter derivatives. By using a method of coupled motion only to identify eight flutter derivations simultaneously, it is also essential to consider correlations among the points that define the full set of flutter derivatives since they are not independent from one another. In this research, an experimental campaign was carried out to obtain the statistical information of flutter derivatives and to assemble the correlation matrix. Several cases of reliability analyses were performed to illustrate the importance of considering correlation among random variables as well as the significance of uncertainty level in flutter derivatives on bridge flutter failure. Moreover, a study of Reliability Based Design Optimization (RBDO) was carried out to see the influence of correlations among flutter derivatives on the optimum designs. The RBDO of a suspension bridge was performed under probabilistic flutter constraint using Reliability Index Approach (RIA) method, and this methodology was applied to the Great Belt East Bridge.},

keywords = {Correlation, Flutter derivatives, Optimization, Random variables, RBDO, Reliability analysis, Suspension bridge},

pubstate = {published},

tppubtype = {article}

}

@article{cid_bengoa_multi-model_2018,

title = {Multi-model reliability-based design optimization of structures considering the intact configuration and several partial collapses},

author = {Clara Cid Bengoa and Aitor Baldomir-García and Santiago Hernández-Ibáñez and Luis E. Romera-Rodríguez},

url = {https://doi.org/10.1007/s00158-017-1789-y},

doi = {10.1007/s00158-017-1789-y},

issn = {1615-1488},

year  = {2018},

date = {2018-03-01},

urldate = {2026-08-04},

journal = {Structural and Multidisciplinary Optimization},

volume = {57},

number = {3},

pages = {977–994},

abstract = {Some structures require keeping a specific safety level even if part of their elements have collapsed. The aim of this paper is to obtain the optimum design of these structures when uncertainty in some parameters that affects to the structural response is also considered. A Reliability-Based Design Optimization (RBDO) problem is formulated in order to minimize the mass of the structure fulfilling probabilistic constraints in both intact and damaged configurations. The proposed methodology combines the formulation of multi-model optimization with RBDO techniques programmed in a Matlab code. Two application examples are presented consisting of a two-dimensional truss structure with stress constraints as well as a curved stiffened panel of an aircraft fuselage subjected to buckling constraints.},

keywords = {Damaged configurations, Fail-safe design, Multi-model, Optimization, RBDO, Reliability index, Uncertainty},

pubstate = {published},

tppubtype = {article}

}

@article{Cancela2005444,

title = {Two-phase multiobjective optimization},

author = {Alberto Cancela and Julián Dorado and Juan R. Rabuñal-Dopico and Alejandro Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-21944444152&partnerID=40&md5=0096ceaadbbd0674df83823911c71761},

issn = {17900832},

year  = {2005},

date = {2005-01-01},

urldate = {2005-01-01},

journal = {WSEAS Transactions on Information Science and Applications},

volume = {2},

number = {5},

pages = {444-449},

abstract = {This work proposes a genetic algorithm (GA) based approach for the search of the Pareto optimal set of a multiobjective optimization problem. First the global population is divided into various subpopulations. The algorithm operation consists of two phases: firstly each subpopulation tries to optimize a different objective; later the algorithm searches for good compromise solutions between objectives. Information is exchanged by means of the migration of individuals during the second phase. A weighted sum is used for fitness calculation. Weight vectors are randomly generated for each selection event, which creates a wide range of search directions. The good behaviour of the proposed algorithm becomes visible in its application to some continuous problems.},

note = {cited By 1},

keywords = {Evolutionary algorithms; Genetic algorithms; Pareto principle; Vectors, Metapopulation evolutionary algorithm; Multiobjective optimization; Pareto optimal set, Optimization},

pubstate = {published},

tppubtype = {article}

}