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A new method for CF morphology distribution evaluation and CFRC property prediction using cascade deep learning
یک روش جدید برای ارزیابی توزیع مورفولوژی CF و پیش بینی ویژگی CFRC با استفاده از یادگیری عمیق آبشاری-2019 This work presents a deep-learning method to characterize the carbon fiber (CF) morphology distribution
in carbon fiber reinforced cement-based composites (CFRC), predict the CFRC properties, and measure the
contributions of different CF morphology distribution directly using X-ray images. Firstly, the components
of CFRC in slices of X-ray images were segmented and identified using a fully convolutional network
(FCN). Then the CF morphology distribution evaluation were conducted based on the results of
the FCN. At last, the prediction of CFRC properties was realized using a cascade deep learning algorithm
and CF morphology distribution results. The results showed that the FCN provided more reasonable segmentation
results for each component in CFRC than traditional methods. CF clustered areas and CF bundles
increased sharply with the increase of CF content, while uniformly dispersed CF areas showed the
opposite trend. The cascade deep learning provided a method to predict the CFRC properties (e.g. resistivity
and bending strength) using X-ray scanning images, which could also quantificationally measure
the contributions of different CF morphology distribution to properties of the CFRC. Therefore, the proposed
method could be regarded as a nondestructive and effective test for CFRC property evaluation. Keywords: Carbon fiber reinforced cement-based | composites | Carbon fiber distribution | Computed tomography | Deep learning | Radial basis function network |
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