Clustering of Asphalt Pavement Maintenance Sections Based on 3D Ground-Penetrating Radar and Principal Component Techniques

• Published in: Buildings (Basel) Vol. 13; no. 7; p. 1752
• Main Authors: Liu, Huimin, Zheng, Jianhao, Yu, Jiangmiao, Xiong, Chunlong, Li, Weixiong, Deng, Jie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: 3D GPR; Analysis; analysis of variance; Asphalt; Asphalt pavements; Classification; Cluster analysis; Clustering; Decision making; Genetic relationship; Ground penetrating radar; internal crack rate; Methods; principal components; Principal components analysis; Radar; Road maintenance; Roads; Variance analysis; China
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings13071752

Asphalt pavement maintenance section classification is an important prerequisite for accurately determining asphalt pavement maintenance needs and formulating accurate maintenance plans. This paper introduces the three-dimensional (3D) ground-penetrating radar (GPR) pavement internal crack rate index on the basis of an original road surface performance data matrix, and the dimensionality of the road section classification data matrix was reduced through the principal component technique. An analysis of variance was used to compare the significance of the differences in the results for road section classification using different clustering methods and different clustering data and to investigate the influence of the clustering method, principal component technique and crack rate index on the maintenance road section classification results. The results showed that the principal component technique could reduce the dimensionality of the data matrix by 33% and retain more than 84% of the information. There was a genetic relationship between the clustering data and the technical characteristics of the classified sub-sections, and the internal crack rate was important for the characterisation of internal defects in asphalt pavement sub-sections and the determination of maintenance needs. The results of section classification varied considerably between clustering methods, and the choice of clustering method had a relationship to the pavement maintenance objectives. The dynamic clustering method combined with principal component analysis could significantly improve the significance of the differences in the clustering results, effectively improving the division of maintenance sections.