Publicado

2021-05-20

Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC

Experimental validation of Artificial Intelligence model for the energy absorption capacity of UHPFRC

DOI:

https://doi.org/10.15446/dyna.v88n217.86961

Palabras clave:

UHPFRC, ensayo de tracción directa, ANN, modelación, capacidad de absorción de energía (es)
UHPFRC, direct tensile test, ANN, modelling, energy absorption capacity (en)

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Autores/as

El artículo investiga la eficiencia de las redes neuronales artificiales (ANN) para la predicción de la capacidad de absorción de energía (g) del concreto de ultra-altas-prestaciones reforzado con fibras (UHPFRC) sometido a tracción directa. Para mejorar el modelo, se dividieron los datos en datos de entrenamiento y testeo. La red se ajustó usando validación k-fold con los datos de entrenamiento y se evaluó con los datos de testeo. El modelo permitió considerar UHPFRC reforzado con una fibra o con mezcla híbrida de dos fibras, de una amplia gama de fibras, tales como fibras de acero rectas, fibras de acero acabadas en gancho, fibras de acero retorcidas, fibras de PVA, fibras de polietileno y fibras de polipropileno. Adicionalmente se realizó una validación experimental de la red. Los resultados demostraron la eficiencia del modelo de acuerdo con los parámetros estadísticos utilizados, así como su precisión y versatilidad para tratar datos nuevos.

This paper investigates the performance of an artificial neural network (ANN) model in predicting the energy absorption capacity (g) of ultra-high-performance fiber reinforced concrete (UHPFRC) under direct tensile test. To avoid overfitting a data division into test and training datasets was carried out. Thereafter the neural networks were trained on the training dataset by using k-fold validation and the result model was evaluated on the test dataset. The model was capable of consider one-fiber or hybrid-two-fibers-blend as reinforced UHPFRC, of a wide range of fibers such as straight steel fibers, hooked end steel fibers, twisted steel fibers, PVA fibers, polyethylene fibers and polypropylene fibers. Experimental works were performed to validate the accuracy of the model on real data. The results demonstrated the efficiency of the model, according to the statistical parameters used for their evaluation, the accuracy and the versatility of the model when new data in considered.

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Cómo citar

IEEE

[1]
J. Abellán García, J. S. Guzmán Guzmán, J. A. Sánchez Díaz, y J. S. Rojas Grillo, «Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC», DYNA, vol. 88, n.º 217, pp. 150–159, may 2021.

ACM

[1]
Abellán García, J., Guzmán Guzmán, J.S., Sánchez Díaz, J.A. y Rojas Grillo, J.S. 2021. Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC. DYNA. 88, 217 (may 2021), 150–159. DOI:https://doi.org/10.15446/dyna.v88n217.86961.

ACS

(1)
Abellán García, J.; Guzmán Guzmán, J. S.; Sánchez Díaz, J. A.; Rojas Grillo, J. S. Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC. DYNA 2021, 88, 150-159.

APA

Abellán García, J., Guzmán Guzmán, J. S., Sánchez Díaz, J. A. & Rojas Grillo, J. S. (2021). Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC. DYNA, 88(217), 150–159. https://doi.org/10.15446/dyna.v88n217.86961

ABNT

ABELLÁN GARCÍA, J.; GUZMÁN GUZMÁN, J. S.; SÁNCHEZ DÍAZ, J. A.; ROJAS GRILLO, J. S. Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC. DYNA, [S. l.], v. 88, n. 217, p. 150–159, 2021. DOI: 10.15446/dyna.v88n217.86961. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/86961. Acesso em: 22 mar. 2026.

Chicago

Abellán García, Joaquín, Juan Sebastián Guzmán Guzmán, Jairo Alfredo Sánchez Díaz, y Julian Santiago Rojas Grillo. 2021. «Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC». DYNA 88 (217):150-59. https://doi.org/10.15446/dyna.v88n217.86961.

Harvard

Abellán García, J., Guzmán Guzmán, J. S., Sánchez Díaz, J. A. y Rojas Grillo, J. S. (2021) «Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC», DYNA, 88(217), pp. 150–159. doi: 10.15446/dyna.v88n217.86961.

MLA

Abellán García, J., J. S. Guzmán Guzmán, J. A. Sánchez Díaz, y J. S. Rojas Grillo. «Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC». DYNA, vol. 88, n.º 217, mayo de 2021, pp. 150-9, doi:10.15446/dyna.v88n217.86961.

Turabian

Abellán García, Joaquín, Juan Sebastián Guzmán Guzmán, Jairo Alfredo Sánchez Díaz, y Julian Santiago Rojas Grillo. «Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC». DYNA 88, no. 217 (mayo 10, 2021): 150–159. Accedido marzo 22, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/86961.

Vancouver

1.
Abellán García J, Guzmán Guzmán JS, Sánchez Díaz JA, Rojas Grillo JS. Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC. DYNA [Internet]. 10 de mayo de 2021 [citado 22 de marzo de 2026];88(217):150-9. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/86961

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CrossRef citations16

1. Andrés Leonardo Neira Medina, Joaquín Abellán García, Nancy Torres Castellanos. (2022). Flexural behavior of environmentally friendly ultra-high-performance concrete with locally available low-cost synthetic fibers. European Journal of Environmental and Civil Engineering, 26(13), p.6281. https://doi.org/10.1080/19648189.2021.1938686.

2. Joaquin Abellan-Garcia, Jaime Fernández-Gómez, M. Iqbal Khan, Yassir M. Abbas, Carlos Pacheco-Bustos. (2023). ANN approach to evaluate the effects of supplementary cementitious materials on the compressive strength of recycled aggregate concrete. Construction and Building Materials, 402, p.132992. https://doi.org/10.1016/j.conbuildmat.2023.132992.

3. N. Marcela Redondo-Pérez, Jesús D. Redondo-Mosquera, Joaquín Abellán-García. (2024). A Comprehensive Overview of Recycled Glass as Mineral Admixture for Circular UHPC Solutions. Sustainability, 16(12), p.5077. https://doi.org/10.3390/su16125077.

4. Joaquín Abellán-García, Juan J. Ortega-Guzmán, Diego A. Chaparro-Ruiz, Eliana García-Castaño. (2022). A Comparative Study of LASSO and ANN Regressions for the Prediction of the Direct Tensile Behavior of UHPFRC. Advances in Civil Engineering Materials, 11(1), p.235. https://doi.org/10.1520/ACEM20210101.

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6. Yaren Aydın, Celal Cakiroglu, Gebrail Bekdaş, Laith Abualigah, Zong Woo Geem. (2025). Predicting the total heating and cooling energy consumption with ultra high-performance fiber reinforced concrete facade panels using explainable machine learning models. Energy Reports, 14, p.3254. https://doi.org/10.1016/j.egyr.2025.10.006.

7. Jesús D. Redondo-Mosquera, David Sánchez-Angarita, Marcela Redondo-Pérez, Juan C. Gómez-Espitia, Joaquín Abellán-García. (2023). Development of high-volume recycled glass ultra-high-performance concrete with high C3A cement. Case Studies in Construction Materials, 18, p.e01906. https://doi.org/10.1016/j.cscm.2023.e01906.

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10. Joaquin Abellan-Garcia, Yassir M. Abbas, Mohammad Iqbal Khan, Francisco Pellicer-Martínez. (2024). ANOVA-guided assessment of waste glass and limestone powder influence on ultra-high-performance concrete properties. Case Studies in Construction Materials, 20, p.e03231. https://doi.org/10.1016/j.cscm.2024.e03231.

11. Joaquín Abellán-García, Juan S. Carvajal-Muñoz, César Ramírez-Munévar. (2024). Application of ultra-high-performance concrete as bridge pavement overlays: Literature review and case studies. Construction and Building Materials, 410, p.134221. https://doi.org/10.1016/j.conbuildmat.2023.134221.

12. Joaquín Abellán-García. (2022). Tensile behavior of recycled-glass-UHPC under direct tensile loading. Case Studies in Construction Materials, 17, p.e01308. https://doi.org/10.1016/j.cscm.2022.e01308.

13. Joaquin Abellan-Garcia, Jaime Fernández, M. Iqbal Khan, Yassir M. Abbas, Julian Carrillo. (2023). Uniaxial tensile ductility behavior of ultrahigh-performance concrete based on the mixture design – Partial dependence approach. Cement and Concrete Composites, 140, p.105060. https://doi.org/10.1016/j.cemconcomp.2023.105060.

14. Joaquín Abellán-García, Jairo A. DSánchez-Díaz, Victoria Eugenia Ospina-Becerra. (2022). Neural network-based optimization of fibres for seismic retrofitting applications of UHPFRC. European Journal of Environmental and Civil Engineering, 26(13), p.6305. https://doi.org/10.1080/19648189.2021.1938687.

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