Title

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.

Department(s)

Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

INSPIRE - University Transportation Center

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.

Keywords and Phrases

Compressive Strength; Hyperspectral Imaging; KNN; Reflectance; SVM; W/C Ratio

International Standard Serial Number (ISSN)

0950-0618

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Elsevier Ltd, All rights reserved.

Publication Date

01 Aug 2020

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