Simulation of self-compacting concrete properties containing silica quicksand using ANN models

Ramin Tabatabaei Mirhosseini (Islamic Azad University, Kerman Branch)
Mohsen Shamsadinei (Islamic Azad University, Kerman Branch)

Article ID: 158



Self-compacting concrete (SCC) mix designs exhibit complexities in their mechanical properties due to composite nature of the material and the multitude and variety of factors that affect such properties. In this paper, a set of SCC mix designs are made using silica quicksand (as filler) instead of rock powder with other required materials. The tests of fresh concrete such as the slump flow, J-ring, V-funnel, L-box tests and the hardened concrete tests are investigated and considered. The tests results are shown that, a high quality has been achieved for SCC mixture contains the quicksand and silica fume contents with low lubricant admixture dosage. The research is embodied the use of a branch of Artificial Neural Networks (ANN) as a quick and reliable alternative to such experimental testing. Results show that the ANN technique can perform as a satisfactory alternative to experimental testing to provide speedy prediction of optimum silica quicksand content must be added prior to SCC mix design. As such, proposed method for the SCC mix design are limited in scope and are approximate at best as they must rely on the results of experimental tests, which are both costly and time-consuming to perform.

Full Text:



[1] Aggarwal, P., Siddique, R., Aggarwal, Y., Gupta, S. and Gupta, M. (2008), “Self-compacting concrete - procedure for mix design”, Leonardo El. J. Pract. Technol., 7(12), 15-24.

[2] Alshihri, M.M., Azmy, A.M., and El-Bisy, M.S. (2009), “Neural networks for predicting compressive strength of structural light weight concrete”, Constr. Build. Mater., 23 (6), 2214-2219.

[3] Andreasen, A.H.M. and Andersen J. (1930), “Ueber die beziehung zwischen kornabstufung und zwischenraum in produkten aus losen ko¨ rnern (mit einigen Experimenten), Kolloid-Zeitschrift, 50 (1), 217– 228 (in German).

[4] Atici, U. (2011), “Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network”, Expert. Sys. Appl., 38 (8), 9609-9618.

[5] Brouwers, H.J.H. and Radix, H.J. (2005), “Self-compacting concrete: theoretical and experimental study”, Cem. Concre. Res., 35 (1), 2116 – 2136.

[6] Chauvin, Y. and Rumelhart, D.E., (1995), “Backpropagation: Theory, Architectures, and Applications. Lawrence Erlbaum Assoc., Inc., Publ., Hillsdale, N.J.

[7] Demir, A. (2015), “Prediction of Hybrid fibre-added concrete strength using artificial neural networks”. Engineering, Comput. Concrete, 15 (4), 503-514.

[8] Gupta, S., (2013), “Using artificial neural network to predict the compressive strength of concrete containing nano-silica”, Civil Eng. Archit., 1(3), 96-102.

[9] Khan, M., Noor, M. and Fazal, R. (2015), “Modeling shotcrete mix design using artificial neural network”, Comput. Concrete, 15 (2), 167-181.

[10] MD Nor, A. and Hanizam, A. (2011), “The compressive and flexural strengths of self-compacting concrete using raw rice husk ash”, J. Eng. Sci. Technol., 6(6), 720-732.

[11] Nagamoto, N. and Ozawa, K. (1997), “Mixture properties of self- compacting, high-performance concrete”, Proceedings of Third CANMET/ACI International Conferences on Design and Materials and Recent advances in Concrete Technology, SP-172, V. M. Malhotra, American Concrete Institute, Farmington Hills, Mich., 623-637.

[12] Okamura, H. (1997), Self-compacting High-Performance Concrete, Concrete International, 19(7), 50- 54.

[13] Okamura, H. and Ouchi, M. (1999), “Self-Compacting Concrete—development, present, and future”, Proceedings of 1st International RILEM Symposium on Self-Compacting Concrete, Stockholm, Sweden.

[14] Okamura, H. and Ouchi, M. (2003), “Self-compacting concrete”, J. Adv. Concre. Technol., 1(1), 5-15.

[15] Rocco, C.G. and Elices, M. (2009), “Effect of aggregate shape on the mechanical properties of a simple concrete”, Eng. Fract. Mech., 76(2), 286-298.

[16] Saridemir M., Ozcan F., Severcan M.H., and Topçu I. B. (2009). “Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic”. Constr. Build. Mater., 23(3), 1279-1286.

[17] Su, N., Hsu, K.C. and Chai, H.W. (2001), “A simple mix design method for self- compacting concrete”, Cem. Concre. Res., 31(12), 1799–1807.

[18] Su, N. and Miao, B. (2003), “A new method for mix design of medium strength concrete with low cement content”, Cem. Concre. Comp., 25(1), 215– 222.


  • There are currently no refbacks.
Copyright © 2019 Journal of Architectural Environment & Structural Engineering Research

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.