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1 |
Moisture dependence of electrical resistivity in under-percolated cement-based composites with multi-walled carbon nanotubes
وابستگی مقاومت الکتریکی به رطوبت در کامپوزیت های پایه سیمانی کم نفوذ با نانولوله های کربنی چند جداره-2021 Cement-based piezoresistive composites have attracted significant attention as smart construction materials for embedding self-sensing capability in concrete infrastructure. Although a number of studies have been conducted using multi-walled carbon nanotubes (MWCNTs) as a functional filler for self-sensing cement-based composites, studies addressing the influence of the internal moisture state on the electrical properties are relatively scant. In this study, we aim to experimentally investigate the effect of internal moisture state on the electrical resistivity of cement-based composites containing MWCNTs as an electrically conductive medium to raise a need for calibration of self-sensing data considering the internal moisture state. To this end, the moisture dependence of electrical resistivity in under-percolated cement-based composites was mainly evaluated, along with other material properties such as strength, shrinkage, and flowability. Results revealed that the electrical resistivity increased almost linearly as the internal relative humidity (IRH) decreased, and the increase was more pronounced below the percolation threshold. In addition, it was found that the strength gained by the microfiller effect of MWCNTs was significantly reduced particularly in under-percolated mixtures, leading to overall strength reductions. Furthermore, this study showed that the more the MWCNT was added, the smaller the flowability was obtained due to the increased viscosity of the mixture. The findings of this study are expected to provide pivotal information for accurate and reliable interpretations of self-sensing data generated by MWCNT-embedded cement-based composites.
Keywords: carbon nanotubes | cement-based composites | electrical resistivity | internal relative humidity | percolation threshold | self-sensing |
مقاله انگلیسی |
2 |
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 |
مقاله انگلیسی |