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Computers and Concrete
  Volume 12, Number 3, September 2013, pages 285-301
DOI: http://dx.doi.org/10.12989/cac.2013.12.3.285
 


Predicting the indirect tensile strength of self-compacting concrete using artificial neural networks
Moosa Mazloom and M.M. Yoosefi

 
Abstract
    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/m3 and 400 kg/m3, 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.
 
Key Words
    concrete; tensile strength; self-compacting; neural networks; perceptron; multi layer perceptron (MLP) and radial basis (RB) networks
 
Address
Moosa Mazloom and M.M. Yoosefi: Department of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
 

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