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

Authors

  • Ramin Tabatabaei Mirhosseini Islamic Azad University, Kerman Branch
  • Mohsen Shamsadinei Islamic Azad University, Kerman Branch

DOI:

https://doi.org/10.30564/jaeser.v1i1.158

Abstract

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.

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How to Cite

Tabatabaei Mirhosseini, R., & Shamsadinei, M. (2019). Simulation of self-compacting concrete properties containing silica quicksand using ANN models. Journal of Architectural Environment & Structural Engineering Research, 1(1), 1–9. https://doi.org/10.30564/jaeser.v1i1.158

Issue

Vol. 1 , Iss. 1 (December 2018)

Article Type

Article

License

Copyright © 2019 Journal of Architectural Environment & Structural Engineering Research

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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