A two-dimensional entropy-based method for detecting the degree of segregation in asphalt mixture. (12th September 2022)

• Record Type: Journal Article
• Authors: Hu, Tao; Yuan, Jing; Zhou, Xinglin; Liu, lu; Ran, Maoping
• Is Part Of: Construction & building materials. Volume 347(2022)
• Publication Date: 2022-09-12
• Subjects: Asphalt mixture segregation -- Pavements -- DIP -- 2D entropy -- Edge detection -- Chunking image; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2022.128450
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: Asphalt mixture segregation detection using image texture features. Otsu algorithm combined with Canny edge detection to obtain aggregate distribution image. The two-dimensional entropy is used to calculate aggregate distribution uniformity. A new indicator "U" is proposed to indicate the degree of asphalt mixture segregation. Abstract: Surface segregation in asphalt mixture caused by the homogeneity of aggregates seriously influences the service life and road performance of asphalt pavements. However, existing segregation detection procedures are cumbersome and the detecting results are heavily influenced by the moisture content of the surface asphalt mixture. Therefore, this paper innovatively proposes two-dimensional (2D) entropy of the chunking image in calculating uniformity complexity (U) to evaluate surface segregation of bituminous mixtures with digital image processing (DIP). Before image chunking, Otsu algorithm and Canny edge detection are properly combined to obtain a more accurate aggregate distribution image. Furthermore, the feasibility of this detecting method is verified through a simulation experiment, a grading comparison experiment, and a noise interference experiment. These experimental results demonstrated that this study presents an efficient approach for the segregation detection of asphalt mixture with high accuracy.