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- W3015540687 abstract "In this study, hyperspectral imagery with two computational algorithms are proposed to classify the type of mortar and assess the in-situ strength of fresh mortar in near real time. Each scanning on a mortar surface includes 30 spatial pixels selected for analysis, each assigned with a light reflectance spectrum over 400–2500 nm. Three groups of mortar samples with a water-to-cement (W/C) ratio of 0.6, 0.5 and 0.4, respectively, were cast and scanned from Day 1 to 14 of curing. Reflectance data at a wavelength range of 1920 nm to 1980 nm, associated with the O–H chemical bond, were extracted and averaged to classify the different mortar types with K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms and to predict their compressive strength from a regression equation. The results showed that the average reflectance increased with time due to water molecules reaction during curing process. The KNN classification model with K = 5 had a prediction accuracy of 70% to 75%, and the SVM classification model with C = 1000 and σ = 10 showed a prediction accuracy of approximately 90%. Therefore, the SVM classification algorithm is recommended for use in mortar classification. The compressive strength is well correlated with the average reflectance with a coefficient of over 0.98." @default.
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- W3015540687 date "2020-08-01" @default.
- W3015540687 modified "2023-10-16" @default.
- W3015540687 title "Hyperspectral imaging features for mortar classification and compressive strength assessment" @default.
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- W3015540687 doi "https://doi.org/10.1016/j.conbuildmat.2020.118935" @default.
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