Concave distribution characterization of asphalt pavement surface segregation using smartphone and image processing based techniques. (27th September 2021)
- Record Type:
- Journal Article
- Title:
- Concave distribution characterization of asphalt pavement surface segregation using smartphone and image processing based techniques. (27th September 2021)
- Main Title:
- Concave distribution characterization of asphalt pavement surface segregation using smartphone and image processing based techniques
- Authors:
- Wan, Tongtong
Wang, Hainian
Feng, Ponan
Diab, Aboelkasim - Abstract:
- Highlights: Concave distribution characteristics (CDC) can evaluate surface segregation. More obvious CDC exhibits higher degree asphalt pavement surface segregation. Image processing method was presented based on selected smartphone. A new indicator " e " was proposed to characterize CDC. The practicability of the proposed methodology was confirmed by field test. Abstract: Surface segregation of bituminous mixtures is a criterion of pavement quality and largely affects the characteristics of the pavement. Simple yet effective evaluation of the surface segregation will enable pavement engineers to tailor timely strategies to mitigate the problem. In this paper, a more efficient image processing method with the aid of smartphone imaging was adopted to rate the segregation level of asphalt pavement surface. Twenty-seven asphalt mixture specimens with vast differences were prepared to acquire different surface images using three types of smartphones. A field test section was chosen to validate the practicability. Furthermore, the Fractal Dimensions (FD, DBC-FD) and Percentage of Concave Distribution Area (PCDA) were used to characterize Concave Distribution Characteristics (CDC) of asphalt pavement surface. Texture Depth (TD) and Mean Texture Depth (MTD) were gained through the sand patch method. It was found that it is an encouraging approach to evaluate the surface segregation based on CDC. The image processing technique relying on the selected smartphone type was proposed byHighlights: Concave distribution characteristics (CDC) can evaluate surface segregation. More obvious CDC exhibits higher degree asphalt pavement surface segregation. Image processing method was presented based on selected smartphone. A new indicator " e " was proposed to characterize CDC. The practicability of the proposed methodology was confirmed by field test. Abstract: Surface segregation of bituminous mixtures is a criterion of pavement quality and largely affects the characteristics of the pavement. Simple yet effective evaluation of the surface segregation will enable pavement engineers to tailor timely strategies to mitigate the problem. In this paper, a more efficient image processing method with the aid of smartphone imaging was adopted to rate the segregation level of asphalt pavement surface. Twenty-seven asphalt mixture specimens with vast differences were prepared to acquire different surface images using three types of smartphones. A field test section was chosen to validate the practicability. Furthermore, the Fractal Dimensions (FD, DBC-FD) and Percentage of Concave Distribution Area (PCDA) were used to characterize Concave Distribution Characteristics (CDC) of asphalt pavement surface. Texture Depth (TD) and Mean Texture Depth (MTD) were gained through the sand patch method. It was found that it is an encouraging approach to evaluate the surface segregation based on CDC. The image processing technique relying on the selected smartphone type was proposed by the error rate of reliability analysis, which was not more than 3% compared to the other used smartphones. A newly developed indicator called e was presented to stand for PCDA. The coefficient of determination between e, FD, DBC-FD and TD/MTD are respectively 0.7958, 0.7882, and 0.7585. In the field validation, the coefficient of determination between e and TD/MTD reaches to 0.8546. Therefore, it was demonstrated that the proposed image processing method can be a promising approach to rate the segregation of asphalt pavement surface. … (more)
- Is Part Of:
- Construction & building materials. Volume 301(2021)
- Journal:
- Construction & building materials
- Issue:
- Volume 301(2021)
- Issue Display:
- Volume 301, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 301
- Issue:
- 2021
- Issue Sort Value:
- 2021-0301-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09-27
- Subjects:
- Asphalt pavement -- Image processing method -- Quality control -- Surface concave distribution -- Fractal dimension -- Sand patch method
Building materials -- Periodicals
624.18 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09500618 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.conbuildmat.2021.124111 ↗
- Languages:
- English
- ISSNs:
- 0950-0618
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3420.950900
British Library DSC - BLDSS-3PM
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