Spatial Representation of GPR Data—Accuracy of Asphalt Layers Thickness Mapping

Scientific paper - Original scientific paper

• Author: Šime Bezina
• Author: Ivica Stančerić
• Author: Josipa Domitrović
• Author: Tatjana Rukavina
• Author's institution: University of Zagreb; Faculty of Civil Engineering; (Transportation Engineering)
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Civil Engineering; Transportation

Abstract (english)

Information on pavement layer thickness is very important for determining bearing capacity, estimating remaining life and strengthening planning. Ground‐penetrating radar (GPR) is a nondestructive testing (NDT) method used for determining the continuous pavement layer thickness in the travel direction. The data obtained with GPR in one survey line is suitable for the needs of repair and rehabilitation planning of roads and highways, but not for wider traffic areas such as airfield pavements. Spatial representation of pavement thickness is more useful for airfield pavements but requires a 3D model. In the absence of 3D GPR, a 3D model of pavement thickness can be created by additional processing of GPR data obtained from multiple survey lines. Five 3D models of asphalt pavements were created to determine how different numbers of survey lines affect their accuracy. The distance between survey lines ranges from 1 to 5 m. The accuracy of the 3D models are determined by comparing the asphalt layer thickness on the model with the values measured on 22 cores. The results, as expected, show that the highest accuracy is achieved for the 3D model created with a distance of 1 m between survey lines, with an average relative error of up to 1.5%. The lowest accuracy was obtained for the 3D model created with a distance of 4 m between the survey lines, with an average relative error of 7.4%.

• Keywords (english): ground‐penetrating radar (GPR); asphalt layer thickness; nondestructive testing (NDT); 3D modelling; spatial representation; airfield pavement; apron
• Language: english
• Publication type: Scientific paper - Original scientific paper
• Publication status: Published
• Peer review: Peer review - international
• Publication version: Published version
• Journal title: Remote Sensing
• Numbering: vol. 13, no. 5, 864
• p-ISSN: 2072-4292
• DOI: https://doi.org/10.3390/rs13050864
• URN:NBN: urn:nbn:hr:237:829091
• Publication: 2021-02-25
• Document URL: https://www.mdpi.com/2072-4292/13/5/864
• Type of resource: Text
• Access conditions: Open access
• Created on: 2024-07-11 06:44:42