Cementitious phase quantification using deep learning
Sheiati, Shohreh; Nguyen, Hoang; Kinnunen, Päivö; Ranjbar, Navid (2023-06-16)
Sheiati, S., Nguyen, H., Kinnunen, P., & Ranjbar, N. (2023). Cementitious phase quantification using deep learning. Cement and Concrete Research, 172, 107231. https://doi.org/10.1016/j.cemconres.2023.107231
© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2023062057017
Tiivistelmä
Abstract
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.
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