A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Identification and evaluation of spalling aggregate in chip seals in three dimensions
Highlights The spatial aggregate stacking leads to the neglect of some spalling areas using image methods. The area of exposed black area obtained by image segmentation is not equal to the area of spalling area due to aggregate spalling. 3-D evaluation data is applied to overcome the influence of aggregate spatial stacking on the identification of the spalling area. Perimeter, area and volume of spalling points are used to quantify the characteristics of aggregate spalling.
Abstract In the context of the present lack of aggregate spalling identification methods and the imprecise recognition of images of aggregate spalling points owing to aggregate stacking and coverage in the course of chip-seal paving, this paper proposes an aggregate spalling recognition method based on 3-D laser elevation data points in order to improve the recognition accuracy of spalling aggregate in chip seals. In this study, 3-D point elevation data on the surface of a chip seal specimen were fed into the 3-D laser detection equipment, the raw data were smoothed, the super four-point fast and robust matching method was used on the 3-D laser points for registration, and 3-D reconstructions of the chip seal were built based on the laser point after registration, and 3-D reconstructions before and after spalling were overlaid with the different values of the surface elevations before and after spalling. The spalling plane curve was then obtained using contours from the differences in the values of the surface elevation before and after spalling, and the spalling perimeter Ln, spalling area Sn, and spalling volume Vn were then calculated on the basis of the 3-D coordinates of the spalling curve. The results showed that the relative errors of the spalling area measured by the laser method and the OTSU method (an image segmentation algorithm proposed by Otsu (1979)) were #1 3.1% vs 4.0%, #2 4.4% vs 6.4%, #3 3.3% vs 58.2%, respectively, indicating that the bitumen attached to the aggregate and the overlap of plane images of aggregate due to aggregate stacking are the main reasons for the greater errors in the recognition of image method. The relative errors in the spalling perimeter of the three specimens are 12.61%, 1.60%, and 0.94%, the relative errors of spalling area are 3.13%, 4.36%, and 3.31%, and the relative errors of spalling volume are 6.69%, 6.57%, and 8.71%, which are less than the approximately same level 10% indicating that the accuracy and the stability of aggregate spalling recognition based on 3-D laser elevation data is excellent.
Identification and evaluation of spalling aggregate in chip seals in three dimensions
Highlights The spatial aggregate stacking leads to the neglect of some spalling areas using image methods. The area of exposed black area obtained by image segmentation is not equal to the area of spalling area due to aggregate spalling. 3-D evaluation data is applied to overcome the influence of aggregate spatial stacking on the identification of the spalling area. Perimeter, area and volume of spalling points are used to quantify the characteristics of aggregate spalling.
Abstract In the context of the present lack of aggregate spalling identification methods and the imprecise recognition of images of aggregate spalling points owing to aggregate stacking and coverage in the course of chip-seal paving, this paper proposes an aggregate spalling recognition method based on 3-D laser elevation data points in order to improve the recognition accuracy of spalling aggregate in chip seals. In this study, 3-D point elevation data on the surface of a chip seal specimen were fed into the 3-D laser detection equipment, the raw data were smoothed, the super four-point fast and robust matching method was used on the 3-D laser points for registration, and 3-D reconstructions of the chip seal were built based on the laser point after registration, and 3-D reconstructions before and after spalling were overlaid with the different values of the surface elevations before and after spalling. The spalling plane curve was then obtained using contours from the differences in the values of the surface elevation before and after spalling, and the spalling perimeter Ln, spalling area Sn, and spalling volume Vn were then calculated on the basis of the 3-D coordinates of the spalling curve. The results showed that the relative errors of the spalling area measured by the laser method and the OTSU method (an image segmentation algorithm proposed by Otsu (1979)) were #1 3.1% vs 4.0%, #2 4.4% vs 6.4%, #3 3.3% vs 58.2%, respectively, indicating that the bitumen attached to the aggregate and the overlap of plane images of aggregate due to aggregate stacking are the main reasons for the greater errors in the recognition of image method. The relative errors in the spalling perimeter of the three specimens are 12.61%, 1.60%, and 0.94%, the relative errors of spalling area are 3.13%, 4.36%, and 3.31%, and the relative errors of spalling volume are 6.69%, 6.57%, and 8.71%, which are less than the approximately same level 10% indicating that the accuracy and the stability of aggregate spalling recognition based on 3-D laser elevation data is excellent.
Identification and evaluation of spalling aggregate in chip seals in three dimensions
Hui, Bing (author) / Zhang, Yandi (author) / Ma, Ziye (author) / Wang, Hainian (author) / Yang, Xu (author)
2022-11-24
Article (Journal)
Electronic Resource
English
ANTI-SPALLING AND HIGH-STRENGTH LIGHTWEIGHT AGGREGATE CONCRETE
| European Patent Office | 2022
Specimens size, aggregate size, and aggregate type effect on spalling of concrete in fire
| British Library Online Contents | 2018
Mechanism and Measures of Coarse Aggregate Spalling in Aged Tunnel Concrete Linings
| British Library Conference Proceedings | 2006
Performance Evaluation of Chip Seals in Idaho
| NTIS | 2010