• Vol 6, No 2 (2015)
  • Civil Engineering

Modeling Slump of Ready Mix Concrete using Artificial Neural Network

Vinay Chandwani, Vinay Agrawal, Ravindra Nagar, Sarbjeet Singh


Published at : 30 Apr 2015
IJtech : IJtech Vol 6, No 2 (2015)
DOI : https://doi.org/10.14716/ijtech.v6i2.213

Cite this article as:

Chandwani, V., Agrawal, V., Nagar, R., Singh, S., 2015. Modeling Slump of Ready Mix Concrete using Artificial Neural Network. International Journal of Technology. Volume 6(2), pp. 207-216

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Vinay Chandwani Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan 302017, India
Vinay Agrawal Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan 302017, India
Ravindra Nagar Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan 302017, India
Sarbjeet Singh Department of Civil Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan 302017, India
Email to Corresponding Author

Abstract
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With rapid growth in the construction industry, Ready Mix Concrete (RMC) is playing a key role in offering customized quality of concrete to contractors and builders. The workability of concrete covers early age operations of concrete viz., placing, compaction and finishing. Since RMC is manufactured at a plant and transported to the construction site, hence the loss of workability is of prime concern due to the considerable time interval between mixing and placing of concrete. Workability of concrete measured using a slump test is an indicator to evaluate the life of RMC during its transportation phase and uniformity of concrete from batch to batch. The concrete mix proportions like cement, fly ash, coarse aggregates, fine aggregates, water and admixtures govern the workability or slump value of the concrete. Artificial Neural Networks (ANNs) learning from past examples gathered from RMC plant has been used to model the functional relationship between the input parameters and the slump value. The ANN model provided promising results compared to first order and second order regression techniques in modeling unknown and complex nature of relationships exhibited by the input parameters and the slump of concrete. The neural network synaptic weights which control the learning mechanism of ANN have been further used to compute the percentage relative importance of each constituent of RMC on the slump value.

Artificial Neural Network, Three-way data split technique, Slump of concrete, Weights Method.

References

Mehta, P.K. and Monteiro, P.J.M., (1993), Concrete-Structure, Properties and Materials, Prentice Hall, Englewood Cliffs, N.J., USA.

Flood, I. and Kartam, N., (1994), Neural Networks in Civil Engineering–II: Systems and Application, Journal of Computing in Civil Engineering, Volume 8, Number 2, pp. 149–162, ASCE.

Haykin, S., Neural Networks A Comprehensive Foundation, (2009), Eighth Ed., Pearson Prentice Hall, India.

Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N. and Chatti, M.A., (2006), Predicting the compressive strength and slump of high strength concrete using neural network, Construction and Building Materials, Volume 20, pp. 779-775, Elsevier.

Yeh, I., (2006), Exploring Concrete Slump Model Using Artificial Neural Networks, Journal of Computing in Civil Engineering, Volume 20, Number 3, pp. 217-221, ASCE.

Yeh, I., (2007), Modeling slump flow of concrete using second-order regressions and artificial neural networks, Cement and Concrete Composites, Volume 29, pp. 474-480, Elsevier.

Aggrawal, Y. and Aggarwal, P., (2011), Prediction of Compressive Strength of SCC Containing Bottom Ash using Artificial Neural Networks, World Academy of Science, Engineering and Technology, Volume 77, pp. 735-740.

Fausett, L.V., (2008), Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Third Ed, Pearson Education.

Gutierrez-Osuna, R., Introduction to Pattern Analysis: Lecture 13 Cross Validation, Texas A&M University. http://www.cs.tau.ac.il/~nin/Courses/NC05/pr_l13.pdf

Tosh, C.R. and Ruxton, G., (2010), Modeling Perception with Artificial Neural Networks, First Ed, Cambridge University Press, Cambridge, U.K.

Montano, J.J. and Palmer A., (2003), Numeric sensitivity analysis applied to feedforward neural networks, Neural Computing and Applications, Volume 12, pp. 119-125.

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