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Cementitious phase quantification using deep learning
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AI
DOI：10.1016/j.cemconres.2023.107231.png)
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Abstract
En 中文
This study investigates deep learning-based backscattered electron (BSE) image segmentation as a novel approach to automatise phase quantification of cementitious materials and estimate their degree of hydration and porosity. The case study was on Portland cement paste that hydrated from 1 day to 2 years. The initial findings suggest that using arbitrary thresholds for phase segmentation, a strong correlation can be established between the results from BSE image analysis, quantitative XRD, and EDS/BSE, particularly for samples with a hydration age >28 days. The second part demonstrates the success of automated image segmentation that relies on learning the material composition from a meticulously analysed image database, which can then predict the content of numerous other images within seconds. This novel approach can turn the analysis of cementitious materials' phase composition from a tedious process that requires specialised equipment and expertise into a routine test for quality control.
Keywords:
Cement
Segmentation
Phase quantification
Deep learning
Microscopy
Journal
IF:
13.1
Papers:
7.0K
Citations:
7.5W
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