Commenced in January 2007
Paper Count: 32146
Prediction of Slump in Concrete using Artificial Neural Networks
Abstract:High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and uniformity requirements that cannot be achieved routinely using conventional constituents and normal mixing, placing, and curing procedures. It is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed to show possible applicability of Neural Networks (NN) to predict the slump in High Strength Concrete (HSC). Neural Network models is constructed, trained and tested using the available test data of 349 different concrete mix designs of High Strength Concrete (HSC) gathered from a particular Ready Mix Concrete (RMC) batching plant. The most versatile Neural Network model is selected to predict the slump in concrete. The data used in the Neural Network models are arranged in a format of eight input parameters that cover the Cement, Fly Ash, Sand, Coarse Aggregate (10 mm), Coarse Aggregate (20 mm), Water, Super-Plasticizer and Water/Binder ratio. Furthermore, to test the accuracy for predicting slump in concrete, the final selected model is further used to test the data of 40 different concrete mix designs of High Strength Concrete (HSC) taken from the other batching plant. The results are compared on the basis of error function (or performance function).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071518Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3328
 P. K Mehta, and P. J. M. Monteiro, "Concrete-Structure, properties and materials," Prentice Hall, Englewood Cliffs, N. J., 1993.
 M. S. Shetty, "Concrete Technology," S. Chand, New Delhi.H., 2005.
 A. Jain, S. K. Jha,, and S. Misra, "Modeling the compressive strength of concrete using Artificial Neural Networks," Indian Concr. J., pp. 17-22, Oct. 2006.
 I. Flood, and N. Kartam, "Neural Networks in Civil Engineering I: Principles and Understanding," J. Comp. in Civil Eng., vol. 8, no. 2, pp. 149-162, 1994.
 D. R. Rehak, C. R. Thewalt, and L. B. Doo, "Neural Network approach in Structural Mechanis Computations," Proc., Structures Congress -89, ASCE, New York, pp. 168-176, 1989.
 A. Jain, A. K. Varshney, and U. C. Joshi, "Short-term water demand forecast modeling at IIT Kanpur using Artificial Neural Networks," Water Resour. Manage., vol. 15, no. 5, pp. 299-321, 2001.
 H. G. Ni, and J. Z. Wang, "Prediction of Compressive Strength of Concrete by Neural Networks," Cem. Concr. Res., vol. 30, no. 8, pp. 1245-1250, 2000.
 T. Ji, T. Lin, and X. Lin, "A Concrete Mix Proportion Design Algorithm based on Artificial Neural Networks," Cem. Conc. Res., vol. 36, no. 7, pp. 1399-1408, 2006.
 A. Cladera, and R. Mari, "Shear Design Procedures for reinforced normal and high strength concrete beams using artificial neural network beams. I: Beams without stirrups," Eng. Struct., vol. 26, no. 7, pp. 917-926, 2004.
 W. P. S. Dias and S. P. Pooliyadda, "Neural Networks for predicting properties of concrete with admixtures," Con. Build. Mat., vol. 15, pp. 371-379, 2000.
 J. Bai, S. Wild, J. A. Ware, and B. B. Sabir, "Using Neural Networks to predict workability of Concrete incorporating metakaolin and Fly Ash," Adv. Eng. Software., vol. 34, no. 11-12,pp. 663-669, 2003.
 M. A. Bhatti, A. Oztas, M. Pala, E. Ozbay, E. Kanca, and N. Caglar, "Predicting the compressive strength and slump of high strength concrete using Neural Network," Conc. Build. Mat., vol. 20, pp. 769- 775, 2005.
 I. C. Yeh, "Exploring Concrete Slump Model Using Artificial Neural Networks," J. Comput. Civil Eng., vol. 20, no. 3, pp. 217-221, 2006.
 G. Bandyopadhyay, and S. Chattopadhyay, "Single Hidden Layer Artificial Neural Network Models Versus Multiple Linear Regression Model in forecasting the Time Series of Total Ozone," Int. J. Environ. Sci. Tech., vol. 4, no. 1, pp. 141-149, 2007.
 R. J. Schalkoff, "Artificial Neural Networks," Mc Graw Hill, Singapore, 1995.
 S. N. Sivanandam, S. Sumathi, and S. N. Deepa, S. N. "Intoduction to Neural Networks using MATLAB 6.0," Tata McGraw-Hill, New Delhi, 2006.