Performance evaluation of mechanical properties of self-compacting concrete using artificial neural network

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    This experimental study was undertaken to investigate the effects of using local materials (cement, fly ash, super-plasticizer, coarse aggre-gate, and sand) on the mechanical properties of Self-Compacting Concrete (SCC). For this purpose, a total of 31 mixtures of SCC were prepared by the neural network design methods. Furthermore, based on the experimental results, the neural network model-based clear for-mulations were developed to predict the mechanical properties of SCC. The test results have shown that mineral admixtures were very effective on hardened properties of SCC. In addition, it was found that the developed model by using neural network appeared to have a high predictive capacity of hardened properties of SCC with respect to regression and experimental.

     

     


  • Keywords


    Hardened Properties; Self-Compacting Concrete; Neural Network; Experimental; Regression.

  • References


      [1] Mehta PK & Monteiro PJM. (1993). Concrete: structure, properties and materials. 2nd Edition. Prentice Hall, ISBN: 0131756214, pp. 548.

      [2] Vengala, Jagadish, Sudarsan, MS & Ranganath RV (2003). Experimental study for obtaining self-compacting concrete. Indian Concrete. Journals, 77, pp.1261-1266.

      [3] Alqadi, Arabi NS, Kdamal Nasharuddin Bin Mustapha, Sivakumar Naganathan & Qahir NS Al-Kadi (2013). Development of self-compacting concrete using contrast constant factorial design. Journal of King Saud University – Engineering Sciences, 25, pp. 105-112.

      [4] Arabi NS AL Qadi, Kamal Nasharuddin Bin Mustapha, Hashem AL-Mattarneh & Qahir N. S. AL-kadi, (2009). Statistical models for hardened properties of self-compacting concrete. American Journal of Engineering and Applied Sciences, 2(4), pp. 764-770.

      [5] Arabi NS Alqadi, Kamal Nasharuddin Bin Mustapha, Sivakumar Naganathan & Qahir NS Al-Kadi (2012). Uses of central composite design and surface response to evaluate the influence of constituent materials on fresh and hardened properties of self-compacting concrete. KSCE Journal of Civil Engineering, 16(3), pp.407-416. DOI 10.1007/s12205-012-1308-z

      [6] Osman, Gencel, Witold Brostow, Cengiz Ozel & Mümin Filiz, (2010). An Investigation on the concrete properties containing colemanite. International Journal of Physical Sciences, 5(3), pp. 216-225, online at http://www.academicjournals.org/IJPS, ISSN 1992 – 1950.

      [7] Ji T, Lin T & Lin X (2006). A Concrete mix proportion design algorithm based on artificial neural networks. Cement and Concrete Research, 36, pp. 1399-1408.

      [8] Oztas A, Pala M, Ozbay E, Kanca E, Caglar AN & Bhatti MA (2006). Predicting the compressive strength and slump of high strength concrete using neural network. Construction and Building Materials, 20, pp. 769-775.

      [9] Bauer M, Buchtala O, Horeis T, Kern R, Sick B & Wagner R (2009), Technical data mining with evolutionary radial basis function classifiers. Appl. Soft Comput. 9(2), pp. 765-774. doi:10.1016/j.asoc.2008.07.007

      [10] Mitra P, Mitra S & Pal SK (2000), Staging of cervical cancer with soft computing, Biomed Eng., IEEE Trans. 47(7), pp. 934–94. doi:10.1109/10.846688

      [11] Al-Batah MS, Mat Isa NA, Zamli KZ & Azizli KA (2010), Modified recursive least squares algorithm to train the hybrid multi-layered perceptron (HMLP) network. Appl. Soft Comput. 10(1), pp. 236–244. doi:10.1016/j.asoc.2009.06.018

      [12] ASTM Standard C 150, Specification for Ordinary Portland Cement, ASTM international.

      [13] ASTM C 33-86 (2006), Specification for Concrete Aggregate, International Standard Worldwide. ASTM, 17, A557, V.04.02.

      [14] ASTM C 618 (2006), Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for use in Concrete. International Standard Worldwide ASTM, 17, A557, V.04.02.

      [15] ASTM C 494-92, Specification for Chemical Admixture for Concrete. ASTM international.

      [16] ASTM 1129, Standard Terminology Relating to Water, 2006 Edition (ASTM-D1129-06a).

      [17] Villiers Jd & Barnard E (1992), Back-propagation neural nets with one and two hidden layers”, IEEE Trans. Neural Netw. 4(1).

      [18] Sonebi Mohammed (2004) Medium Strength Self-Compacting Concrete Containing Fly Ash: Modeling Using Factorial Experimental Plans. Cement and Concrete Research, 34, pp. 1199-1208.

      [19] MINITAB Handbook, Fourth Edition (2003), A Supplementary Text That Teaches Basic Statistics Using MINITAB”, http:www Minitab.com /products /Minitab /14/ documentation.aspx.


 

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Article ID: 28462
 
DOI: 10.14419/ijet.v9i1.28462




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