Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network
作者： T Chandra Sekhara Reddy , None
DOI: 10.1007/S11709-017-0445-3
关键词: Artificial neural network 、 Curing (chemistry) 、 Ultimate tensile strength 、 Flexural strength 、 Composite material 、 Silica fume 、 Slurry 、 Materials science 、 Metakaolin 、 Compressive strength
摘要: This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 mixes, and specimens cast tested after 28 days curing. obtained experimental data are trained using ANN which consists 4 input parameters like Percentage fiber (PF), Aspect Ratio (AR), Type admixture (TA) (PA). corresponding output compressive strength, tensile flexural strength. predicted values show a good correlation between data. performance 4-14-3 architecture was better than other architectures. It concluded that highly powerful tool suitable for assessing characteristics SIFCON.
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