Rodríguez-Sánchez, Alejandro E.
Main Affiliation
Preferred name
Rodríguez-Sánchez, Alejandro E.
Official Name
Rodríguez-Sánchez, Alejandro Esteban
ORCID
0000-0003-3397-5261
Scopus Author ID
57202719691
40 results
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Item type:Publication, An orthotropic hyperfoam-based model for the compressive response of aged polyurethane foams<jats:title>Abstract</jats:title> <jats:p>This study presents a constitutive modeling framework to characterize the uniaxial compressive response of polyurethane foams subjected to nine distinct accelerated aging conditions. A new orthotropic, three-term Hyperfoam-based model was simultaneously calibrated for two principal material directions using previously published experimental data. The unified model was successfully fitted to the experimental stress-strain curves, achieving coefficients of determination greater than 0.78 in all cases. This demonstrates its capability to capture the material’s characteristic response, even under severe degradation that significantly increases experimental variability. Analysis of energy absorption and efficiency revealed that while the model is accurate for mild to moderate degradation states, deviations are observed at high strains (above a compressive strain of 0.7) for severely degraded samples. Crucially, the analysis of the fitted orthotropic response quantitatively demonstrates that severe aging accentuates the foam’s mechanical anisotropy. This work establishes a quantitative link between aging conditions, the evolution of mechanical properties, and the orthotropic constitutive parameters, providing a predictive tool for evaluating the material’s directional performance.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ensemble learning for mechanical behavior modeling of 3D-printed PLA under tension(Springer Science and Business Media LLC, 2026-02-09); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Numerical analysis of an
<i>Opuntia</i>
-based biopolymer: Hyperelastic calibration and FEA implementation(SAGE Publications, 2026-04-17); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Experimental characterization and tensile mechanical modeling of an Opuntia velutina biopolymer(Springer Science and Business Media LLC, 2025-12-12); Sandra Pascoe-Ortiz - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Uncertainty quantification of hyperelastic models for polystyrene and polypropylene foams via conformal prediction(IOP Publishing, 2026-05-04); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design, Construction and Validation of a Vibration Table with Distributed Load Cells for Dynamic Measurement(Springer Nature Switzerland, 2025-11-18); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparative assessment of physics-informed recurrent networks for modeling rate- and density-dependent compression in expanded polystyrene foams<jats:p>This study systematically compares Recurrent Neural Network architectures—namely simple Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Units—for modeling the cyclic compressive mechanical response of Expanded Polystyrene foam across varying densities and loading rates. Purely data-driven (direct) methodologies and Physics-Informed Neural Network formulations, the latter with explicit physics enforcement, were investigated using experimental data from uniaxial cyclic compression tests. The objective was to predict the first Piola-Kirchhoff stress as a function of time, compressive stretch, initial density of the materials, and loading rate. Results demonstrated that direct Gated Recurrent Units and Long Short-Term Memory models consistently achieved the highest predictive accuracy, evidenced by low Mean Absolute Error and high coefficient of determination values, and exhibited superior generalization capabilities on unseen test conditions. While Physics-Informed Neural Network models, particularly those incorporating boundary conditions and energy restrictions, offered enhanced physical consistency—such as enforcing zero strain energy density at a unitary stretch and enforcing positive strain energy—they incurred greater computational expense and, in certain configurations, showed reduced predictive accuracy or stability, especially during generalization. The findings conclude that direct Gated Recurrent Units and Long Short-Term Memory architectures provide an effective and efficient approach for accurately capturing the complex, history-dependent behaviour of Expanded Polystyrene foam under cyclic loading.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimización de la configuración de materiales compuestos laminados mediante redes neuronales y templado simulado(CULCyT: Cultura Científica y Tecnológica, 2024); Julio César Galvis ChacónLa optimización de materiales compuestos laminados es uno de los principales desafíos en el diseño de componentes o sistemas estructurales debido a la influencia de múltiples parámetros en su desempeño y respuesta mecánica ante la deformación. Esta investigación utiliza un metamodelo basado en redes neuronales artificiales para predecir índices de desempeño, específicamente el índice de falla de Tsai-Wu, a partir de la configuración del laminado de un material compuesto sujeto a cargas considerando espesores y orientaciones de sus fibras. El metamodelo alimenta una función objetivo diseñada para mejorar la configuración de una pieza mediante la optimización de las orientaciones de las fibras. Se combina un algoritmo de templado simulado adaptado para materiales compuestos laminados con redes neuronales, generando un espacio de soluciones que ofrece al diseñador una amplia gama de opciones para abordar el análisis del problema. El método reportado es una alternativa eficiente al método tradicional de análisis de materiales compuestos laminados, agilizando el proceso y ampliando las posibilidades de configuración disponibles para su selección.9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A machine learning approach to estimate the strain energy absorption in expanded polystyrene foamsTraditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters. </jats:p>Scopus© Citations 13 26 1