Evaluation of Spectral Characteristics of Asphalt Mixtures
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 9
Abstract
Evaluation of the pavement condition is expensive, time-consuming, and labor-intensive and becomes even more challenging in remote areas. Nondestructive remote-sensing techniques can enable pavement condition assessment over a large areal extent. Remote-sensing sensors, capable of acquiring the emitted and reflected energies of the target with respect to the wavelength, can help in the identification and characterization of various asphalt mixtures prepared at a laboratory or field scale. This study aims at studying the spectral signature of asphalt mixtures with respect to aggregate gradation, binder type, binder concentration, aging, moisture conditions, and distress. In addition, an attempt has been made to identify the correlation between spectral features and asphalt mixture properties utilizing spectral metrics such as the Visible (VIS2) index and Shortwave Infrared (SWIR) index in visible and shortwave infrared regions of the electromagnetic spectrum, respectively. Indian and US specifications were followed for the fabrication and simulation of various states/conditions of asphalt mixtures. It was found from the analysis of spectral signatures that characteristic absorption features present between 1,700 and 2,300 nm can be used to identify different asphalt mixtures with distinct binder types and aggregate gradations. Also, variations in the intensity of these features were detected following various conditioning and distress simulations. Moreover, the statistical analysis indicated that the distressed samples exhibit a higher magnitude of spectral metrics (VIS2/SWIR) compared with the undamaged samples. The spectral characteristics of asphalt mixtures under different material compositions identified in the study offer great potential for pavement surface condition assessment through hyperspectral remote sensing.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The authors acknowledge the financial support of the Defence Geoinformatics Research Establishment, DRDO, Ministry of Defence, Government of India, under the project code CESPNDTRL01140xRIB005. Authors would also like to acknowledge Mrs. Ritu Anilkumar for providing the support to conduct the study.
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Published In
Journal of Materials in Civil Engineering
Volume 35 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 27, 2022
Accepted: Feb 10, 2023
Published online: Jun 21, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 21, 2023
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