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dc.contributor.authorAbellán García, Joaquín
dc.contributor.authorFernández Gómez, Jaime A.
dc.contributor.authorTorres Castellanos, Nancy
dc.contributor.authorNúñez López, Andrés M.
dc.date.accessioned2021-11-06T14:31:46Z
dc.date.available2021-11-06T14:31:46Z
dc.date.issued2020
dc.identifier.isbn9783030584818
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/1811
dc.description.abstractTo 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.abstractPara 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.extent13 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherSpringer Naturespa
dc.relation.ispartofseriesRILEM;Vol. 30
dc.relation.ispartofseriesBEFIB 2020: Fibre Reinforced Concrete: Improvements and Innovations;
dc.rights© RILEM 2021eng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.titleMachine Learning Prediction of Flexural Behavior of UHPFRCeng
dc.typeCapítulo - Parte de Librospa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_14cbspa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.contributor.researchgroupEstructuras y Materialesspa
dc.identifier.doi10.1007/978-3-030-58482-5_52
dc.publisher.placeSwitzerlandspa
dc.relation.citationendpage583spa
dc.relation.citationstartpage570spa
dc.relation.indexedN/Aspa
dc.relation.ispartofbookRILEM Bookserieseng
dc.relation.referencesAbellan, 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.referencesAbellan, 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.referencesZhang, 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.referencesGhafari, 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.referencesACI Committe 239, ACI – 239 Committee in Ultra-High Performance Concrete (2018)spa
dc.relation.referencesMeng, W., Samaranayake, V.A., Khayat, K.H.: Factorial design and optimization of UHPC with lightweight sand. ACI Mater. J. (2018).spa
dc.relation.referencesAbellán-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.referencesViet, 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.referencesLi, 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.referencesHuang, 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.referencesAbellán-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.referencesAbellán, 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.referencesChandwani, 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.referencesAbellán-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.referencesAbellán-García, J.: Four-layer perceptron approach for strength prediction of UHPC, Constr. Build. Mater. 256 (2020).spa
dc.relation.referencesKhashman, 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.referencesR Core Team, “R: A Language and Environment for Statistical Computing,” Vienna, Austria (2018).spa
dc.relation.referencesAbellán, 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.referencesAtkinson, A., Riani, M.: Robust Diagnostic Regression Analysis. Springer, US, New York (2000)spa
dc.relation.referencesHärdle, W.K., Simar, L.: Applied Multivariate Statistical Analysis. Springer-Verlag GmbH, Berlin (2012)spa
dc.relation.referencesEveritt, B., Hothorn, T., MVA: An Introduction to Applied Multivariate Analysis with R (2015)spa
dc.relation.referencesMax 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.referencesRosenblatt, F.: The Perceptron: A probabilistic model for information storage and orgnization in the brain. Cornerr Aeronaut. Lab. 65, 386–408 (1958)spa
dc.relation.referencesGhafari, 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.referencesEstebon, M.D., Perceptrons : An Associative Learning Network, Virginia Tech (1997)spa
dc.relation.referencesGupta, 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.referencesAderaw, 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.referencesAdeli, H.: Neural networks in civil engineering: 1989–2000. Comput. Civ. Infrastruct. Eng. 16, 126–142 (2001)spa
dc.relation.referencesChandwani, 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.referencesTaghaddos, 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.referencesChollet, F., Allaire, J.J.: Deep Learning with R. Manning Publications Co, New Jersey (2018)spa
dc.relation.referencesJames, G., Witen, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R (2007).spa
dc.relation.referencesKaiser, H.F.: The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960)spa
dc.relation.referencesMoriasi, 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.referencesSrinivasulu, 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.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.subject.armarcAprendizaje automático (Inteligencia artificial)spa
dc.subject.armarcMachine learningeng
dc.subject.armarcHormigón armadospa
dc.subject.armarcReinforced concreteeng
dc.subject.armarcAnálisis estructural (Ingeniería)spa
dc.subject.armarcStructural analysis (Engineering)eng
dc.subject.armarcFlexibilidadspa
dc.subject.armarcFlexureeng
dc.subject.armarcResistencia de materialesspa
dc.subject.armarcStrength of materialseng
dc.subject.proposalUHPFRCeng
dc.subject.proposalLOPeng
dc.subject.proposalMOReng
dc.subject.proposalMachine learningeng
dc.subject.proposalPCAeng
dc.type.coarhttp://purl.org/coar/resource_type/c_3248spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookPartspa
dc.type.redcolhttps://purl.org/redcol/resource_type/CAP_LIBspa


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