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A platform for research: civil engineering, architecture and urbanism
Cementitious phase quantification using deep learning
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 greater than 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.
Cementitious phase quantification using deep learning
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 greater than 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.
Cementitious phase quantification using deep learning
Sheiati, Shohreh (author) / Nguyen, Hoang (author) / Kinnunen, Paivo (author) / Ranjbar, Navid (author)
2023-01-01
Sheiati , S , Nguyen , H , Kinnunen , P & Ranjbar , N 2023 , ' Cementitious phase quantification using deep learning ' , Cement and Concrete Research , vol. 172 , 107231 . https://doi.org/10.1016/j.cemconres.2023.107231
Article (Journal)
Electronic Resource
English
Cementitious phase quantification using deep learning
| Elsevier | 2023
. The challenge of quantification of cementitious systems
| British Library Conference Proceedings | 2004