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Developing an artificial neural network model for predicting concrete’s compression strength and electrical resistivity
Desarrollo de un modelo de redes neuronales artificiales para predecir la resistencia a la compresión y la resistividad eléctrica del concreto
DOI:
https://doi.org/10.15446/ing.investig.v27n1.14771Keywords:
neural network, concrete strength, concrete resistivity, concrete ultrasonic pulse velocity (en)redes neuronales, resistencia a la compresión del concreto, resistividad del concreto, velocidad de pulso en el concreto (es)
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The present study was conducted for predicting the compressive strength of concrete based on unit weight ultrasonic and pulse velocity (UPV) for 41 different concrete mixtures. This research emerged from the need for a rapid test for predicting concrete’s compressive strength. The research was also conducted for predicting concrete’s electrical resistivity based on unit weight ultrasonic, pulse velocity (UPV) and compressive strength with the same mixes. The prediction was made using simple regression analysis and artificial neural networks. The results revealed that artificial neural networks can be used for effectively predicting compressive strength and electrical resistivity.
En esta investigación se busca obtener un método para predecir la resistencia a la compresión mediante el peso unitario y la velocidad de pulso ultrasónico usando 41 mezclas de concreto diferentes. El estudio ha sido por la necesidad de obtener un método rápido para predecir la resistencia a la compresión del concreto. De la misma manera, la investigación también busca predecir la resistividad eléctrica del concreto mediante el peso unitario, la velocidad de pulso ultrasónico y la resistencia a la compresión. El modelo para predecir se realizó utilizando una regresión simple y un modelo de redes neuronales. Los resultados mostraron que los modelos de redes neuronales para predecir la resistencia a la compresión y la resistividad eléctrica del concreto funcionan adecuadamente.
References
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Graham, L. D. Forbes, D. R. and Smith, S., Modeling the ready mixed concrete delivery system with neural networks., Automation in Construction, Vol. 15, No. 5, 2006, pp. 656-663. DOI: https://doi.org/10.1016/j.autcon.2005.08.003
Hilera, J. R, y Martínez, V. J., Redes neuronales Artificiales: Fundamentos, Modelos y Aplicaciones., Castilla Editores, 1997.
Mindess, F. Y. and Darwin, C., Second Edition, Prentice Hall, 2003.
Obert, L., Measurement of pressures on rock pillars in underground mines., R.I. 3521, U.S., Bureau of Mines, 1940.
Portland Cement Association., Electrical Resistivity of Concrete - A Literature Review., PCA R&D, Serial No 2457, 2003.
Ramachandran, V. S. and Beaudoin, J. J., Handbook of analytical techniques in concrete science and technology - principles, techniques, and applications., Edited by Institute for Research in Construction National Research Council, Ottawa, Canada, 1999.
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Copyright (c) 2007 Juan Manuel Lizarazo Marriaga, José Gabriel Gómez Cortés
This work is licensed under a Creative Commons Attribution 4.0 International License.
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