Prediction of Electrical Conductivity of Fiber-Reinforced Cement-Based Composites by Deep Neural Networks.

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Abstract

This study presents a deep-learning method for characterizing carbon fiber (CF) distribution and predicting electrical conductivity of CF-reinforced cement-based composites (CFRCs) using scanning electron microscopy (SEM) images. First, SEM images were collected from CFRC specimens with different CF contents. Second, a fully convolutional network (FCN) was utilized to extract carbon fiber components from the SEM images. Then, DSEM and Dsample were used to evaluate the distribution of CFs. DSEM and Dsample reflected the real CF distribution in an SEM observation area and a specimen, respectively. Finally, a radial basis neural network was used to predict the electrical conductivity of the CFRC specimens, and its weights (di) were used to evaluate the effects of CF distribution on electrical conductivity. The results showed that the FCN could accurately segment CFs in SEM images with different magnifications. Dsample could accurately reflect the morphological distribution of CFs in CFRC. The electrical conductivity prediction errors were less than 6.58%. In addition, di could quantitatively evaluate the effect of CF distribution on CFRC conductivity.