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

Dongdong Yuan,Zheng Tong,Jie Gao,Wanli Ye,Wei Jiang,Jingjing Xiao

doi:10.3390/ma12233868

Dongdong Yuan, Zheng Tong + Show 4 more

Open Access

https://doi.org/10.3390/ma12233868

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Journal: Materials	Publication Date: Nov 23, 2019
Citations: 7	License type: CC BY 4.0

Affiliation: Chang'an University, East China Jiaotong University

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.

Highlights

Carbon fiber (CF)-reinforced cement-based composites (CFRCs) are widely used in civil engineering because of their excellent electrical conductivity [3]
From the point of view of deep learning, as the results of the fully convolutional network (FCN) are directly used for the radial basis neural network (RBNN), the carbon fiber (CF) distribution in each scanning electron microscopy (SEM) image can be regarded as a low-level feature to form the conductivity property of a CFRC, which is a high-level feature
The average Precision, Recall, and F-Measure of the FCN were 0.956, 0.927, and 0.938, respectively. This indicates that the FCN could accurately segment CFs in SEM images

Summary

Introduction

Carbon fiber (CF)-reinforced cement-based composites (CFRCs) are widely used in civil engineering (e.g., ice–snow melting pavements [1] and self-monitoring structures [2]) because of their excellent electrical conductivity [3]. It is difficult to control and predict the electrical conductivity of CFRCs owing to their complex features, the CF content, conductivities of cement and CFs [4], and CF distribution [5]. With the development of measurement technologies, the conductivities of cement and CFs can be calibrated with high precision. The challenge and importance of this issue have led researchers to develop many techniques to evaluate CF distribution and predict the electrical conductivity of CFRCs. At present, there are three approaches to evaluate CF distribution. This method can only indirectly characterize the distribution by measuring the content of fibers in the samples. A similar problem can be found in the electric

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Conclusion