Dr. Joaquín Irazábal González
International Center for Numerical Methods in Engineering, Barcelona, Catalonia, Spain
Assistant Research Professor
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Academic Career Length
12Years (2015-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 May 2017 in Computers and Geotechnics
Journal Article
Joaquín Irazábal GonzálezFSFernando Salazar
Fernando Salazar
EOEugenio Oñate
Eugenio Oñate
8 September 2015 in International Journal for Numerical and Analytical Methods in Geomechanics
Journal Article
Fernando SalazarJoaquín Irazábal González
Antonia LareseEOE. Oñate
E. Oñate
Journal Article
Fernando SalazarAVAndre Conde Vazquez
Andre Conde VazquezJoaquín Irazábal González
David J. Vicente GonzálezDam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multi-class, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.
11 June 2020 in Archives of Computational Methods in Engineering
Journal Article
Fernando SalazarDavid J. Vicente GonzálezJoaquín Irazábal GonzálezIDIgnasi De-Pouplana
Ignasi De-PouplanaJMJavier San Mauro
Javier San Mauro5 January 2023 in Computational Particle Mechanics
Journal Article
Joaquín Irazábal GonzálezFSFernando Salazar
Fernando Salazar
DVDavid J. Vicente
David J. Vicente
The discrete element method (DEM) is well suited for calculating the behaviour of bulk materials. However, its application is limited because of the cumbersome calibration process required. Trial and error calibration can be useful for the computation of single outputs, but is unfeasible when the aim is reproducing more complex phenomena with high accuracy. This paper describes an iterative procedure based on machine learning to automatically calibrate the parameters of DEM models for reproducing the behaviour of bulk materials. The performance of the methodology is assessed by its application to the calibration of a DEM model to compute the stress–strain evolution of a cohesive material under uniaxial compression. In this case, a random forest model is used in conjunction with the iterative calibration algorithm proposed. The results of this study show that the algorithm is accurate and flexible for the calibration of material parameters.
1 July 2016 in Computational Particle Mechanics
Journal Article
MSMiquel Santasusana
Miquel SantasusanaJoaquín Irazábal GonzálezEOEugenio Oñate
Eugenio Oñate
JCJ. M. Carbonell
J. M. CarbonellIn this work, we present a new methodology for the treatment of the contact interaction between rigid boundaries and spherical discrete elements (DE). Rigid body parts are present in most of large-scale simulations. The surfaces of the rigid parts are commonly meshed with a finite element-like (FE) discretization. The contact detection and calculation between those DE and the discretized boundaries is not straightforward and has been addressed by different approaches. The algorithm presented in this paper considers the contact of the DEs with the geometric primitives of a FE mesh, i.e. facet, edge or vertex. To do so, the original hierarchical method presented by Horner et al. (J Eng Mech 127(10):1027–1032, 2001) is extended with a new insight leading to a robust, fast and accurate 3D contact algorithm which is fully parallelizable. The implementation of the method has been developed in order to deal ideally with triangles and quadrilaterals. If the boundaries are discretized with another type of geometries, the method can be easily extended to higher order planar convex polyhedra. A detailed description of the procedure followed to treat a wide range of cases is presented. The description of the developed algorithm and its validation is verified with several practical examples. The parallelization capabilities and the obtained performance are presented with the study of an industrial application example.
About Me
My professional career is mainly focused on the field of structural calculation using numerical methods. In November 2011 I started working at the International Center for Numerical Methods in Engineering (CIMNE) in Madrid developing research projects in Civil Engineering. The projects addressed the possibilities of using particle methods for the calculation of the behaviour of granular materials, by means of the Particle Finite Element Method (PFEM) and the Discrete Element Method (DEM). I am a member of the Kratos (Open-Source framework for the implementation of numerical methods for the solution of engineering problems written in C++ and Python) development team. Belonging to it I have learned that version control programs (Git) are essential for efficient collaboration. Between 2013 and 2015 I studied the Master of Numerical Methods in Engineering at the Technical University of Catalonia (UPC). Then I completed my PhD thesis entitled 'Numerical analysis of railway ballast behaviour using the Discrete Element Method', presented on 6 October 2017, also at the UPC. In the thesis, which was awarded the XVI Talgo Prize for Technological Innovation, the Discrete Element Method was applied to calculate the performance of railway ballasted tracks. Since 2019, has expanded his skill set by leading projects that integrate these advanced computational techniques with data science and Machine Learning to develop innovative engineering solutions.
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