Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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 (Autor:in) / Nguyen, Hoang (Autor:in) / Kinnunen, Paivo (Autor:in) / Ranjbar, Navid (Autor:in)
01.01.2023
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
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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