Modeling and Analysis of Concrete Slump Using Artificial Neural Networks
Publication: Journal of Materials in Civil Engineering
Volume 20, Issue 9
Abstract
Artificial neural network (ANN) and regression models are developed for the estimation of concrete slump using concrete constituent data. The concrete mix constituent and slump data from laboratory tests have been employed to develop all models. The results obtained in this study demonstrate the superiority of the ANN models. It was found that combining one or more concrete mix constituents and treating them as an independent input variable is not advantageous when using regression but can be very useful when using ANNs for modeling concrete slump. Sensitivity analyses based on the ANN models were carried out to evaluate the impact of different concrete mix constituents on the slump values. It was found that the slump attains a minimum value at the critical levels of mortar and coarse aggregates, and tends to increase with paste content and decrease with sand content in the concrete mix.
Get full access to this article
View all available purchase options and get full access to this article.
References
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
Cladera, A., and Mari, R. (2004). “Shear design procedures for reinforced normal and high-strength concrete beams using artificial neural networks. I: Beams without stirrups.” Eng. Struct., 26(7), 917–926.
Jain, A., and Indurthy, S. K. V. P. (2003). “Comparative analysis of event based rainfall-runoff modeling techniques—Deterministic, statistical, and artificial neural networks.” J. Hydrol. Eng., 8(2), 93–98.
Jain, A., Jha, S. K., and Misra, S. (2006). “Modelling compressive strength of concrete using artificial neural networks.” Indian Concr. J., October, 17–22.
Jain, A., and Ormsbee, L. E. (2002). “Evaluation of short-term water demand forecast modeling techniques: Conventional methods versus AI.” J. Am. Water Works Assoc., 94(7), 64–72.
Jain, A., and Srinivasulu, S. (2004). “Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms, and artificial neural network techniques.” Water Resour. Res., 40, W04302.
Jain, A., Varshney, A. K., and Joshi, U. C. (2001). “Short-term water demand forecast modeling at IIT Kanpur using artificial neural networks.” Water Resour. Manage., 15(5), 299–321.
Ni, H. G., and Wang, J. Z. (2000). “Prediction of compressive strength of concrete by neural networks.” Cem. Concr. Res., 30(8), 1245–1250.
Rajasekaran, S., and Lee, S. C. (2003). “Prediction of concrete strength using serial functional network model.” Struct. Eng. Mech., 16(1), 83–99.
Rehak, D. R., Thewalt, C. R., and Doo, L. B. (1989). “Neural network approach in structural mechanics computations.” Proc., Structures Congress ’89, ASCE, New York, 168–176.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning representations by back-propagating errors.” Nature (London), 323, 533–536.
Zhao, Z. Y., and Ren, L. Q. (2002). “Failure criterion of concrete under triaxial stresses using neural networks.” Comput. Aided Civ. Infrastruct. Eng., 17(1), 68–73.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 20 • Issue 9 • September 2008
Pages: 628 - 633
Copyright
© 2008 ASCE.
History
Received: Apr 22, 2005
Accepted: Nov 28, 2007
Published online: Sep 1, 2008
Published in print: Sep 2008
Notes
Note. Associate Editor: Jason Weiss
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.