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Machine Learning Prediction of Flexural Behavior of UHPFRC
dc.contributor.author | Abellán García, Joaquín | |
dc.contributor.author | Fernández Gómez, Jaime A. | |
dc.contributor.author | Torres Castellanos, Nancy | |
dc.contributor.author | Núñez López, Andrés M. | |
dc.date.accessioned | 2021-11-06T14:31:46Z | |
dc.date.available | 2021-11-06T14:31:46Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 9783030584818 | |
dc.identifier.uri | https://repositorio.escuelaing.edu.co/handle/001/1811 | |
dc.description.abstract | To evaluate the possibility of predicting the flexural behaviour of UHPFRC, four analytical models were developed, based on artificial neural networks (ANN), to predict the first cracking tension or Limit of Proportionality (LOP), its corresponding deflection (δLOP), ultimate strength or Modulus of Rupture (MOR), and its corresponding deflection (δMOR) of UHPFRC under bending test. The models that were composed of an input level, one output level, and four hidden levels were developed through the R platform. The input level applied the most significative Principal Components (PC) of a large dimension of input dataset. To avoid overfitting K-fold validation and l2 regularization was used. After the models were created, an improvement based on assembling of models by incorporating the predicted values in the dataset of features. The results indicated that the developed assembling models have a good accuracy for the prediction of the behaviour of UHPFRC under three or four points bending test, even when containing supplementary cementitious materials and hybrid mixture of fibers. | eng |
dc.description.abstract | Para evaluar la posibilidad de predecir el comportamiento a flexión del UHPFRC, se desarrollaron cuatro modelos analíticos, basados en redes neuronales artificiales (ANN), para predecir la primera tensión de fisuración o Límite de Proporcionalidad (LOP), su correspondiente deflexión (δLOP), la resistencia última o Módulo de Ruptura (MOR), y su correspondiente deflexión (δMOR) del UHPFRC bajo ensayo de flexión. Los modelos, compuestos por un nivel de entrada, un nivel de salida y cuatro niveles ocultos, se desarrollaron mediante la plataforma R. El nivel de entrada aplicó los componentes principales (PC) más significativos de un conjunto de datos de entrada de gran dimensión. Para evitar el sobreajuste se utilizó la validación K-fold y la regularización l2. Una vez creados los modelos, se realizó una mejora basada en el ensamblaje de los modelos mediante la incorporación de los valores predichos en el conjunto de datos de características. Los resultados indicaron que los modelos de ensamblaje desarrollados tienen una buena precisión para la predicción del comportamiento de los UHPFRC en ensayos de flexión de tres o cuatro puntos, incluso cuando contienen materiales cementosos suplementarios y mezcla híbrida de fibras. | spa |
dc.format.extent | 13 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | Springer Nature | spa |
dc.relation.ispartofseries | RILEM;Vol. 30 | |
dc.relation.ispartofseries | BEFIB 2020: Fibre Reinforced Concrete: Improvements and Innovations; | |
dc.rights | © RILEM 2021 | eng |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | spa |
dc.title | Machine Learning Prediction of Flexural Behavior of UHPFRC | eng |
dc.type | Capítulo - Parte de Libro | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_14cb | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.contributor.researchgroup | Estructuras y Materiales | spa |
dc.identifier.doi | 10.1007/978-3-030-58482-5_52 | |
dc.publisher.place | Switzerland | spa |
dc.relation.citationendpage | 583 | spa |
dc.relation.citationstartpage | 570 | spa |
dc.relation.indexed | N/A | spa |
dc.relation.ispartofbook | RILEM Bookseries | eng |
dc.relation.references | Abellan, J., Torres, N., Núñez, A., Fernández, J.: Ultra high preformance fiber reinforced concrete: state of the art, applications and possibilities into the latin american market. In: XXXVIII Jornadas Sudam. Ing. Estructural, Lima, Peru (2018) | spa |
dc.relation.references | Abellan, J., Torres, N., Núñez, A., Fernández, J.: Influencia del exponente de Fuller, la relación agua conglomerante y el contenido en policarboxilato en concretos de muy altas prestaciones, In: IV Congr. Int. Ing. Civ., Havana, Cuba (2018) | spa |
dc.relation.references | Zhang, J., Zhao, Y.: Experimental investigation and prediction of compressive strength of ultra-high performance concrete (UHPC) containing supplementary cementitious materials. Hindawi Adv. Mater. Sci. Eng. 2017, 522–525 (2017). | spa |
dc.relation.references | Ghafari, E., Bandarabadi, M., Costa, H., Júlio, E.: Prediction of fresh and hardened state properties of UHPC: Comparative study of statistical mixture design and an artificial neural network model. J. Mater. Civ. Eng. 27, 04015017 (2015). | spa |
dc.relation.references | ACI Committe 239, ACI – 239 Committee in Ultra-High Performance Concrete (2018) | spa |
dc.relation.references | Meng, W., Samaranayake, V.A., Khayat, K.H.: Factorial design and optimization of UHPC with lightweight sand. ACI Mater. J. (2018). | spa |
dc.relation.references | Abellán-García, J., Núñez-López, A., Torres-Castellanos, N., Fernández-Gómez, J.: Factorial design of reactive powder concrete containing electric arc slag furnace and recycled glass powder. Dyna. 87, 42–51 (2020). | spa |
dc.relation.references | Viet, T.A.V., Ludwig, H.M.: Proportioning optimization of uhpc containing rice husk ash and ground granulated blast-furnace slag. In: Schmidt, M., Fehling, E., Glotzbach, C., Fröhlich, S., Piotrowski, S. (Eds.) 3rd International Symposium. UHPC Nanotechnology Construction Materials, Kassel, Germany, pp. 197–205 (2012) | spa |
dc.relation.references | Li, W., Huang, Z., Zu, T., Shi, C., Duan, W.H., Shah, S.P.: Influence of nanolimestone on the hydration, mechanical strength, and autogenous shrinkage of ultrahigh-performance concrete. J. Mater. Civ. Eng. 28, 1–9 (2016). | spa |
dc.relation.references | Huang, Z., Cao, F.: Effects of Nano-materials on the Performance of UHPC, 材料导报B:研究篇. 26 136–141 (2012) | spa |
dc.relation.references | Abellán-García, J., Núñez-López, A., Torres-Castellanos, N., Fernández-Gómez, J.: Effect of FC3R on the properties of ultra-high-performance concrete with recycled glass • Efecto del FC3R en las propiedades del concreto de ultra altas prestaciones con vidrio reciclado. Dyna. 86, 84–92 (2019). | spa |
dc.relation.references | Abellán, J., Fernández, J., Torres, N., Núñez, A.: Statistical optimization of ultra-high-performance glass concrete. ACI Mater. J. 117, 243–254 (2020). | spa |
dc.relation.references | Chandwani, V., Agrawal, V., Nagar, R.: Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks, Expert Syst. Appl. 42 (2015) 885–893. | spa |
dc.relation.references | Abellán-García, J., Fernández-Gómez, J., Torres-Castellanos, N.: Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks. Eur. J. Environ. Civ. Eng 1–25.10.1080/19648189.2020.1762749 (2020) | spa |
dc.relation.references | Abellán-García, J.: Four-layer perceptron approach for strength prediction of UHPC, Constr. Build. Mater. 256 (2020). | spa |
dc.relation.references | Khashman, A., Akpinar, P.: ScienceDirect non-destructive prediction of concrete compressive strength using neural networks prediction of concrete compressive strength using neural networks. Proc. Comput. Sci. 108, 2358–2362 (2017). | spa |
dc.relation.references | R Core Team, “R: A Language and Environment for Statistical Computing,” Vienna, Austria (2018). | spa |
dc.relation.references | Abellán, J., Torres, N., Núñez, A., Fernández, J.: Quality optimization of low-cost UHPC using micro limestone powder and glass flour, Comput. Concr. (n.d.) | spa |
dc.relation.references | Atkinson, A., Riani, M.: Robust Diagnostic Regression Analysis. Springer, US, New York (2000) | spa |
dc.relation.references | Härdle, W.K., Simar, L.: Applied Multivariate Statistical Analysis. Springer-Verlag GmbH, Berlin (2012) | spa |
dc.relation.references | Everitt, B., Hothorn, T., MVA: An Introduction to Applied Multivariate Analysis with R (2015) | spa |
dc.relation.references | Max Kuhn Contributions from Jed Wing, A., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., Hunt, T., Max Kuhn, M., Package “caret” Classification and Regression Training Description Misc functions for training and plotting classification and regression models, CRAN - R Repos (2017). | spa |
dc.relation.references | Rosenblatt, F.: The Perceptron: A probabilistic model for information storage and orgnization in the brain. Cornerr Aeronaut. Lab. 65, 386–408 (1958) | spa |
dc.relation.references | Ghafari, E., Al.: Optimization of UHPC by Adding Nanomaterials. In: Proceedings of Hipermat 2012, in 3rd International. Symposium. UHPC Nanotechnology Construction. Material., Kassel Uni, Kassel, Alemania, pp. 71–78 (2012) | spa |
dc.relation.references | Estebon, M.D., Perceptrons : An Associative Learning Network, Virginia Tech (1997) | spa |
dc.relation.references | Gupta, S.: Using artificial neural network to predict the compressive strength of concrete containing nano-silica. Civ. Eng. Archit. 1, 96–102 (2013). | spa |
dc.relation.references | Aderaw, M., Muse, S., Abiero, Z.C.: Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Constr. Build. Mater. 190, 517–525 (2018). | spa |
dc.relation.references | Adeli, H.: Neural networks in civil engineering: 1989–2000. Comput. Civ. Infrastruct. Eng. 16, 126–142 (2001) | spa |
dc.relation.references | Chandwani, V., Nagar, R.: Applications of artificial neural networks in modeling compressive strength of concrete: a state of the art review. Int. J. Curr. Eng. Technol. 4, 2949–2956 (2014) | spa |
dc.relation.references | Taghaddos, H., Mahmoudzadeh, F., Pourmoghaddam, A., Shekarchizadeh, M.: Prediction of compressive strength behaviour in RPC with applying an adaptive network-based fuzzy interface system. In: Proceeding International Symposium Ultra High Performance Concr., Kassel, Alemania (2004) | spa |
dc.relation.references | Chollet, F., Allaire, J.J.: Deep Learning with R. Manning Publications Co, New Jersey (2018) | spa |
dc.relation.references | James, G., Witen, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R (2007). | spa |
dc.relation.references | Kaiser, H.F.: The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960) | spa |
dc.relation.references | Moriasi, D.N., Arnold, J.G., Liew, M.W.V., Bingner, R.L., Harmel, R.D., Veith, T.L.: MODEL evaluation guidelines for systematic quantification of accuracy in watershed simulations. Am. Soc. Agric. Biol. Eng. 50, 885–900 (2007) | spa |
dc.relation.references | Srinivasulu, S., Jain, A.: A comparative analysis of training methods for artificial neural network rainfall – runoff models. Appl. Soft Comput. 6, 295–306 (2006). | spa |
dc.rights.accessrights | info:eu-repo/semantics/closedAccess | spa |
dc.rights.creativecommons | Atribución 4.0 Internacional (CC BY 4.0) | spa |
dc.subject.armarc | Aprendizaje automático (Inteligencia artificial) | spa |
dc.subject.armarc | Machine learning | eng |
dc.subject.armarc | Hormigón armado | spa |
dc.subject.armarc | Reinforced concrete | eng |
dc.subject.armarc | Análisis estructural (Ingeniería) | spa |
dc.subject.armarc | Structural analysis (Engineering) | eng |
dc.subject.armarc | Flexibilidad | spa |
dc.subject.armarc | Flexure | eng |
dc.subject.armarc | Resistencia de materiales | spa |
dc.subject.armarc | Strength of materials | eng |
dc.subject.proposal | UHPFRC | eng |
dc.subject.proposal | LOP | eng |
dc.subject.proposal | MOR | eng |
dc.subject.proposal | Machine learning | eng |
dc.subject.proposal | PCA | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_3248 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/bookPart | spa |
dc.type.redcol | https://purl.org/redcol/resource_type/CAP_LIB | spa |
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Clasificación: A - Convocatoria 2018.