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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer-Verlag

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DOI

10.1007/s11661-000-0267-z

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1433-3058

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Computeraided prediction of the AlSubscript2/S

Authors: Ali Nazari Shadi Riahi
Publish Date: 2011/07/20
Volume: 21, Issue: 7, Pages: 1651-1667
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Abstract

In the present paper two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing Al2O3 nanoparticles have been developed at different ages of curing For purpose of building these models training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted The data used in the multilayer feedforward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content nanoparticle content aggregate type water content the amount of superplasticizer the type of curing medium Age of curing and number of testing try According to these input parameters in the neural networks and genetic programming models the split tensile strength and percentage of water absorption values of concretes containing Al2O3 nanoparticles were predicted The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing Al2O3 nanoparticles It has been found that NN and GEP models will be valid within the ranges of variables In neural networks model as the training and testing ended when minimum error norm of network gained the best results were obtained and in genetic programming model when 4 gens was selected to construct the model the best results were acquired Although neural network have predicted better results genetic programming is able to predict reasonable values with a simpler method rather than neural network

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