All Issue

2024 Vol.13, Issue 2 Preview Page

Scientific Paper

Evaluation of asphalt pavement condition using hyper-spectral images

초분광 영상을 이용한 아스팔트 포장 공용성 평가

Kyu-dong Jeong1*
,
Jinhwan Jang2
,
Sung Pil Shin3
,
Jung-il Shin4
정 규동1*
,
장 진환2
,
신 성필3
,
신 정일4
1Senior Researcher, Korea Institute of Civil Engineering and Building Technology
2Research Fellow, Korea Institute of Civil Engineering and Building Technology
3Senior Researcher, Korea Institute of Civil Engineering and Building Technology
4Associate Research Fellow, Seoul Institute
1한국건설기술연구원 도로교통연구본부 수석연구원
2한국건설기술연구원 도로교통연구본부 연구위원
3한국건설기술연구원 도로교통연구본부 수석연구원
4서울연구원 디지털도시연구실 부연구위원
*Corresponding Author
17 January 2024. pp. 322-328
PDF XML Citation
Abstract

The spectral characteristics were evaluated using cored samples collected from cracks on the asphalt pavement. Among the spectral index models, VISI2 and VISR2 had high significance. When the asphalt pavement crack was large, there was a significance of 0.9 or more compared to the sample without crack. Asphalt pavement was investigated using road survey vehicles and drone hyper-spectral cameras and the correlation was evaluated. As a result, rutting showed a relatively high correlation in the WBI model at 0.704, but the correlation was low in relation to IRI and cracks.

Keywords
Hyper-Spectral
VIS
Spectrometer
Pavement Condition
Drone
References
1
Cho, Y. and Bae, S. (2023). "Analysis of Pavement Performance Data for Determining the Initial Application of Preventive Maintenance of Pavements", Journal of the Korean Society of Road Engineers, 25(5), pp. 93-100. 10.7855/IJHE.2023.25.5.093
2
Jeong, K.-D., Jang, J., Shin, S.P. and Shin, J.-I. (2022). "A basic study on the correlation between asphalt pavement aging and hyperspectral image", Journal of the Korean Asphalt Institute, 12(2), pp. 327-334.
3
Kim, S.H., Lee, K.S., Ma, J.R. and Kook, M.J. (2005). "Current status of hyperspectral remote sensing:principle, data processing techniques, and applications", Korean Journal of Remote Sensing, 21(4), pp. 341-369.
4
Lee, Y.J. Jeon, S.I. and Kim, R.E. (2021). "A Computer-vision-based classification of road surface roughness grade using Machine Learning Techniques", Journal of the Korean Asphalt Institute, 23(4), pp. 75-81. 10.24056/KAJ.2021.01.002
Information
  • Publisher :KOREAN ASPHALT INSTITUTE
  • Publisher(Ko) :한국아스팔트학회
  • Journal Title :Journal of the Korean Asphalt Institute
  • Journal Title(Ko) :한국아스팔트학회지
  • Volume : 13
  • No :2
  • Pages :322-328
  • Received Date : 2023-12-12
  • Revised Date : 2023-12-22
  • Accepted Date : 2023-12-26
  • DOI :https://doi.org/10.22702/jkai.2023.13.2.26
Journal Informaiton Journal of the Korean Asphalt Institute Journal of the Korean Asphalt Institute
Online submission
  • KISTI Current Status
  • KISTI Cited-by
  • KCI 문헌 유사도 검사
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close