Hyperspectral Imaging Features for Mortar Classification and Compressive Strength Assessment
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.
Recommended Citation
L. Fan et al., "Hyperspectral Imaging Features for Mortar Classification and Compressive Strength Assessment," Construction and Building Materials, vol. 251, Elsevier, Aug 2020.
The definitive version is available at https://doi.org/10.1016/j.conbuildmat.2020.118935
Department(s)
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Keywords and Phrases
Hyperspectral imaging; W/C ratio; Reflectance; KNN; SVM; Compressive strength
International Standard Serial Number (ISSN)
0950-0618
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Elsevier, All rights reserved.
Publication Date
10 Aug 2020
Comments
Financial support to complete this study was provided by the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (OST-R) under the Auspices of the INSPIRE University Transportation Center under Grant No. 69A3551747126 at Missouri University of Science and Technology.