Machine Learning Prediction of Flexural Behavior of UHPFRC

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...

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Autores:
Abellán García, Joaquín
Fernández Gómez, Jaime A.
Torres Castellanos, Nancy
Núñez López, Andrés M.
Tipo de recurso:
Part of book
Fecha de publicación:
2020
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
eng
OAI Identifier:
oai:repositorio.escuelaing.edu.co:001/1811
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/1811
Palabra clave:
Aprendizaje automático (Inteligencia artificial)
Hormigón armado
Análisis estructural (Ingeniería)
Flexibilidad
Resistencia de materiales
Machine learning
Reinforced concrete
Structural analysis (Engineering)
Flexure
Strength of materials
UHPFRC
LOP
MOR
Machine learning
PCA
Rights
closedAccess
License
© RILEM 2021
id ESCUELAIG2_8450aa3d54c69febfe84877c0c733405
oai_identifier_str oai:repositorio.escuelaing.edu.co:001/1811
network_acronym_str ESCUELAIG2
network_name_str Repositorio Institucional ECI
repository_id_str
dc.title.eng.fl_str_mv Machine Learning Prediction of Flexural Behavior of UHPFRC
title Machine Learning Prediction of Flexural Behavior of UHPFRC
spellingShingle Machine Learning Prediction of Flexural Behavior of UHPFRC
Aprendizaje automático (Inteligencia artificial)
Hormigón armado
Análisis estructural (Ingeniería)
Flexibilidad
Resistencia de materiales
Machine learning
Reinforced concrete
Structural analysis (Engineering)
Flexure
Strength of materials
UHPFRC
LOP
MOR
Machine learning
PCA
title_short Machine Learning Prediction of Flexural Behavior of UHPFRC
title_full Machine Learning Prediction of Flexural Behavior of UHPFRC
title_fullStr Machine Learning Prediction of Flexural Behavior of UHPFRC
title_full_unstemmed Machine Learning Prediction of Flexural Behavior of UHPFRC
title_sort Machine Learning Prediction of Flexural Behavior of UHPFRC
dc.creator.fl_str_mv Abellán García, Joaquín
Fernández Gómez, Jaime A.
Torres Castellanos, Nancy
Núñez López, Andrés M.
dc.contributor.author.none.fl_str_mv Abellán García, Joaquín
Fernández Gómez, Jaime A.
Torres Castellanos, Nancy
Núñez López, Andrés M.
dc.contributor.researchgroup.spa.fl_str_mv Estructuras y Materiales
dc.subject.armarc.spa.fl_str_mv Aprendizaje automático (Inteligencia artificial)
Hormigón armado
Análisis estructural (Ingeniería)
Flexibilidad
Resistencia de materiales
topic Aprendizaje automático (Inteligencia artificial)
Hormigón armado
Análisis estructural (Ingeniería)
Flexibilidad
Resistencia de materiales
Machine learning
Reinforced concrete
Structural analysis (Engineering)
Flexure
Strength of materials
UHPFRC
LOP
MOR
Machine learning
PCA
dc.subject.armarc.eng.fl_str_mv Machine learning
Reinforced concrete
Structural analysis (Engineering)
Flexure
Strength of materials
dc.subject.proposal.eng.fl_str_mv UHPFRC
LOP
MOR
Machine learning
PCA
description 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.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-11-06T14:31:46Z
dc.date.available.none.fl_str_mv 2021-11-06T14:31:46Z
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
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dc.identifier.isbn.none.fl_str_mv 9783030584818
dc.identifier.uri.none.fl_str_mv https://repositorio.escuelaing.edu.co/handle/001/1811
dc.identifier.doi.none.fl_str_mv 10.1007/978-3-030-58482-5_52
identifier_str_mv 9783030584818
10.1007/978-3-030-58482-5_52
url https://repositorio.escuelaing.edu.co/handle/001/1811
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofseries.none.fl_str_mv RILEM;Vol. 30
BEFIB 2020: Fibre Reinforced Concrete: Improvements and Innovations;
dc.relation.citationendpage.spa.fl_str_mv 583
dc.relation.citationstartpage.spa.fl_str_mv 570
dc.relation.indexed.spa.fl_str_mv N/A
dc.relation.ispartofbook.eng.fl_str_mv RILEM Bookseries
dc.relation.references.spa.fl_str_mv 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)
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)
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).
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).
ACI Committe 239, ACI – 239 Committee in Ultra-High Performance Concrete (2018)
Meng, W., Samaranayake, V.A., Khayat, K.H.: Factorial design and optimization of UHPC with lightweight sand. ACI Mater. J. (2018).
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).
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)
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).
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)
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).
Abellán, J., Fernández, J., Torres, N., Núñez, A.: Statistical optimization of ultra-high-performance glass concrete. ACI Mater. J. 117, 243–254 (2020).
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.
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)
Abellán-García, J.: Four-layer perceptron approach for strength prediction of UHPC, Constr. Build. Mater. 256 (2020).
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).
R Core Team, “R: A Language and Environment for Statistical Computing,” Vienna, Austria (2018).
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.)
Atkinson, A., Riani, M.: Robust Diagnostic Regression Analysis. Springer, US, New York (2000)
Härdle, W.K., Simar, L.: Applied Multivariate Statistical Analysis. Springer-Verlag GmbH, Berlin (2012)
Everitt, B., Hothorn, T., MVA: An Introduction to Applied Multivariate Analysis with R (2015)
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).
Rosenblatt, F.: The Perceptron: A probabilistic model for information storage and orgnization in the brain. Cornerr Aeronaut. Lab. 65, 386–408 (1958)
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)
Estebon, M.D., Perceptrons : An Associative Learning Network, Virginia Tech (1997)
Gupta, S.: Using artificial neural network to predict the compressive strength of concrete containing nano-silica. Civ. Eng. Archit. 1, 96–102 (2013).
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).
Adeli, H.: Neural networks in civil engineering: 1989–2000. Comput. Civ. Infrastruct. Eng. 16, 126–142 (2001)
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)
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)
Chollet, F., Allaire, J.J.: Deep Learning with R. Manning Publications Co, New Jersey (2018)
James, G., Witen, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R (2007).
Kaiser, H.F.: The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960)
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)
Srinivasulu, S., Jain, A.: A comparative analysis of training methods for artificial neural network rainfall – runoff models. Appl. Soft Comput. 6, 295–306 (2006).
dc.rights.eng.fl_str_mv © RILEM 2021
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spelling Abellán García, Joaquíndd5fea4042d594753a0a7a56ecf61490600Fernández Gómez, Jaime A.606eed4c55326ee3a41c6f0cfa178152600Torres Castellanos, Nancy2b475ecd9ea004cd3b18c2eaf60c01d1600Núñez López, Andrés M.f81375618f0b4d910607dd89b5cfad0e600Estructuras y Materiales2021-11-06T14:31:46Z2021-11-06T14:31:46Z20209783030584818https://repositorio.escuelaing.edu.co/handle/001/181110.1007/978-3-030-58482-5_52To 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.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.13 páginasapplication/pdfengSpringer NatureSwitzerlandRILEM;Vol. 30BEFIB 2020: Fibre Reinforced Concrete: Improvements and Innovations;583570N/ARILEM BookseriesAbellan, 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)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)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).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).ACI Committe 239, ACI – 239 Committee in Ultra-High Performance Concrete (2018)Meng, W., Samaranayake, V.A., Khayat, K.H.: Factorial design and optimization of UHPC with lightweight sand. ACI Mater. J. (2018).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).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)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).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)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).Abellán, J., Fernández, J., Torres, N., Núñez, A.: Statistical optimization of ultra-high-performance glass concrete. ACI Mater. J. 117, 243–254 (2020).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.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)Abellán-García, J.: Four-layer perceptron approach for strength prediction of UHPC, Constr. Build. Mater. 256 (2020).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).R Core Team, “R: A Language and Environment for Statistical Computing,” Vienna, Austria (2018).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.)Atkinson, A., Riani, M.: Robust Diagnostic Regression Analysis. Springer, US, New York (2000)Härdle, W.K., Simar, L.: Applied Multivariate Statistical Analysis. Springer-Verlag GmbH, Berlin (2012)Everitt, B., Hothorn, T., MVA: An Introduction to Applied Multivariate Analysis with R (2015)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).Rosenblatt, F.: The Perceptron: A probabilistic model for information storage and orgnization in the brain. Cornerr Aeronaut. Lab. 65, 386–408 (1958)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)Estebon, M.D., Perceptrons : An Associative Learning Network, Virginia Tech (1997)Gupta, S.: Using artificial neural network to predict the compressive strength of concrete containing nano-silica. Civ. Eng. Archit. 1, 96–102 (2013).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).Adeli, H.: Neural networks in civil engineering: 1989–2000. Comput. Civ. Infrastruct. Eng. 16, 126–142 (2001)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)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)Chollet, F., Allaire, J.J.: Deep Learning with R. Manning Publications Co, New Jersey (2018)James, G., Witen, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R (2007).Kaiser, H.F.: The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960)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)Srinivasulu, S., Jain, A.: A comparative analysis of training methods for artificial neural network rainfall – runoff models. Appl. Soft Comput. 6, 295–306 (2006).© RILEM 2021https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/closedAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_14cbMachine Learning Prediction of Flexural Behavior of UHPFRCCapítulo - Parte de Libroinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttps://purl.org/redcol/resource_type/CAP_LIBhttp://purl.org/coar/version/c_970fb48d4fbd8a85Aprendizaje automático (Inteligencia artificial)Hormigón armadoAnálisis estructural (Ingeniería)FlexibilidadResistencia de materialesMachine learningReinforced concreteStructural analysis (Engineering)FlexureStrength of materialsUHPFRCLOPMORMachine learningPCAORIGINALMachine Learning Prediction of Flexural Behavior of UHPFRC.pdfMachine Learning Prediction of Flexural Behavior of UHPFRC.pdfapplication/pdf1042137https://repositorio.escuelaing.edu.co/bitstream/001/1811/6/Machine%20Learning%20Prediction%20of%20Flexural%20Behavior%20of%20UHPFRC.pdf9696c06bac1bce1aff3041d69e5fee7cMD56metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/1811/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessTEXTMachine Learning Prediction of Flexural Behavior of UHPFRC.pdf.txtMachine Learning Prediction of Flexural Behavior of UHPFRC.pdf.txtExtracted texttext/plain2https://repositorio.escuelaing.edu.co/bitstream/001/1811/4/Machine%20Learning%20Prediction%20of%20Flexural%20Behavior%20of%20UHPFRC.pdf.txtd784fa8b6d98d27699781bd9a7cf19f0MD54metadata only accessTHUMBNAILFibre reinforced concrete improvements and innovations.pngFibre reinforced concrete improvements and innovations.pngimage/png367221https://repositorio.escuelaing.edu.co/bitstream/001/1811/5/Fibre%20reinforced%20concrete%20improvements%20and%20innovations.png2715e97412f7e38895d8013911bcae55MD55open accessMachine Learning Prediction of Flexural Behavior of UHPFRC.pdf.jpgMachine Learning Prediction of Flexural Behavior of UHPFRC.pdf.jpgGenerated Thumbnailimage/jpeg12204https://repositorio.escuelaing.edu.co/bitstream/001/1811/7/Machine%20Learning%20Prediction%20of%20Flexural%20Behavior%20of%20UHPFRC.pdf.jpg8924a19bd2ac66c8af31e283bd27779aMD57metadata only access001/1811oai:repositorio.escuelaing.edu.co:001/18112022-11-01 03:01:00.712metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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