Computers and Concrete

  • 제12권3호
  • /
  • Pages.285-301
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  • 2013
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Predicting the indirect tensile strength of self-compacting concrete using artificial neural networks

  • Mazloom, Moosa (Department of Civil Engineering, Shahid Rajaee Teacher Training University) ;
  • Yoosefi, M.M. (Department of Civil Engineering, Shahid Rajaee Teacher Training University)
  • 투고 : 2011.03.03
  • 심사 : 2013.03.29
  • 발행 : 2013.09.01
⟨ 이전 논문 다음 논문 ⟩

초록

This paper concentrates on the results of experimental work on tensile strength of self-compacting concrete (SCC) caused by flexure, which is called rupture modulus. The work focused on concrete mixes having water/binder ratios of 0.35 and 0.45, which contained constant total binder contents of 500 $kg/m^3$ and 400 $kg/m^3$, respectively. The concrete mixes had four different dosages of a superplasticizer based on polycarboxylic with and without silica fume. The percentage of silica fume that replaced cement in this research was 10%. Based upon the experimental results, the existing equations for anticipating the rupture modulus of SCC according to its compressive strength were not exact enough. Therefore, it is decided to use artificial neural networks (ANN) for anticipating the rupture modulus of SCC from its compressive strength and workability. The conclusion was that the multi layer perceptron (MLP) networks could predict the tensile strength in all conditions, but radial basis (RB) networks were not exact enough in some circumstances. On the other hand, RB networks were more users friendly and they converged to the final networks quicker.

키워드

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