PURPOSES : Local governments in Korea, including Incheon city, have introduced the pavement management system (PMS). However, the verification of the repair time and repair section of roads remains difficult owing to the non-existence of a systematic data acquisition system. Therefore, data refinement is performed using various techniques when analyzing statistical data in diverse fields. In this study, clustering is used to analyze PMS data, and correlation analysis is conducted between pavement performance and influencing factors.
METHODS : First, the clustering type was selected. The representative clustering types include K-means, mean shift, and density-based spatial clustering of applications with noise (DBSCAN). In this study, data purification was performed using DBSCAN for clustering. Because of the difficulty in determining a threshold for high-dimensional data, multiple clustering, which is a type of DBSCAN, was applied, and the number of clustering was set up to two. Clustering for the surface distress (SD), rut depth (RD), and international roughness index (IRI) was performed twice using the number of frost days, the highest temperature, and the average temperature, respectively.
RESULTS : The clustering result shows that the correlation between the SD and number of frost days improved significantly. The correlation between the maximum temperature factor and precipitation factor, which does not indicate multicollinearity, improved. Meanwhile, the correlation between the RD and highest temperature improved significantly. The correlation between the minimum temperature factor and precipitation factor, which does not exhibit multicollinearity, improved considerably. The correlation between the IRI and average temperature improved as well. The correlation between the low- and high-temperature precipitation factors, which does not indicate multicollinearity, improved.
CONCLUSIONS : The result confirms the possibility of applying clustering to refine PMS data and that the correlation among the pavement performance factors improved. However, when applying clustering to PMS data refinement, the limitations must be identified and addressed. Furthermore, clustering may be applicable to the purification of PMS data using AI.

ABSTRACT
1. 서론
2. 데이터 클러스터링
    2.1. 클러스터링의 종류 및 DBSCAN 장단점
    2.2. DBSCAN 적용 후 산점도
3. 클러스터링 전·후 상관성 분석 비교
    3.1. 클러스터링 전·후 상관성 분석
4. 결론
REFERENCES

• 이수형(한국건설기술연구원 도로교통연구본부 수석연구원) | Lee Soo Hyoung
• 이재훈(인하대학교 공과대학 스마트시티공학과 석사과정) | Lee Jae Hoon
• 김연태(한국건설기술연구원 도로교통연구본부 전임연구원) | Kim Yeon Tae
• 정진훈(인하대학교 공과대학 사회인프라공학과 교수) | Jeong Jin Hoon Corresponding author