Low-Cost and Contactless Survey Technique for Rapid Pavement Texture Assessment Using Mobile Phone Imagery

Zhenlong Gong, Marco Bruno, Margherita Pazzini (), Anna Forte, Valentina Alena Girelli, Valeria Vignali and Claudio Lantieri
Additional contact information
Zhenlong Gong: National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
Marco Bruno: Department of Civil, Chemical, Environmental and Material Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
Margherita Pazzini: Department of Civil, Chemical, Environmental and Material Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
Anna Forte: Department of Civil, Chemical, Environmental and Material Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
Valentina Alena Girelli: Department of Civil, Chemical, Environmental and Material Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
Valeria Vignali: Department of Civil, Chemical, Environmental and Material Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
Claudio Lantieri: Department of Civil, Chemical, Environmental and Material Engineering (DICAM), University of Bologna, 40136 Bologna, Italy

Sustainability, 2024, vol. 16, issue 22, 1-19

Abstract: Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings.

Keywords: pavement texture assessment; close-range photogrammetry; structured-light scanner; 3D image analysis; sustainable road maintenance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/22/9630/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/22/9630/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:22:p:9630-:d:1514412

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().