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
Now showing 1 - 10 of 40
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Item type:Publication, Modeling Planar Flexible Linkages with Cosserat Rods and Neural Networks(Springer Nature Switzerland, 2025-11-18); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - 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, 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
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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, 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, 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, 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, Deep Learning Automated Measurements of Expanded Polystyrene Beads Size Using Low‐Resolution Micrography<jats:title>ABSTRACT</jats:title><jats:p>The analysis of microscopic characteristics of closed‐cell polymeric foams, particularly bead size, is relevant for understanding properties such as thermal insulation, energy absorption, and compressive structural strength of these materials. This study presents an automated method based on Deep Learning models to measure the bead size of Expanded Polystyrene foams in low‐resolution micrographs. The results of this approach were compared with manual measurements at two expanded polystyrene foam densities: 8.5 and 24 kg/m<jats:sup>3</jats:sup>. Hypothesis tests, including Student's <jats:italic>t</jats:italic>‐test, Levene's test, and Mann–Whitney <jats:italic>U</jats:italic> test, were conducted and showed no significant differences between manual and automatic measurements. Student's <jats:italic>t</jats:italic>‐test and Levene's test indicated that both methods have comparable means and variances, while the Two One‐Sided Test confirmed that they were equivalent for bead size measurement. Additionally, the Mann–Whitney <jats:italic>U</jats:italic> test revealed no differences in medians, and Bland–Altman plot analyses demonstrated no systematic bias between the methods. Taken together, these results suggest that the proposed Deep Learning‐based method is a reliable and precise substitute for the manual method in measuring the bead size of expanded polystyrene, making it suitable for practical use in the bead microstructural analysis of expanded polystyrene material.</jats:p>