Classification of Flexible Pavements Based on Texture Data. Issue 12 (December 2022)

• Record Type: Journal Article
• Authors: Sabillon, Christian A.; Hernandez, Joaquin; Bassil, Maria; Prozzi, Jorge
• Is Part Of: Transportation research record. Volume 2676:Issue 12(2022)
• Page Start: 140
• Page End: 151
• Publication Date: 2022-12
• Subjects: infrastructure -- pavements -- pavement surface properties and vehicle interaction -- general -- pavement profile -- pavement texture; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221091560
• Languages: English
• ISSNs: 0361-1981

Abstract

The strong correlations between pavement texture and other surface characteristics—such as friction, drainage, and noise—are widely known. However, those relationships are not straightforward and have a strong dependency on the type and properties of the pavement surface. Incorporating surface information into a prediction model for skid resistance has the potential to drastically increase its predictive power. Nonetheless, in practice, pavement surface information is often unknown at the network level and must be inferred based on local expertise. The objective of this study was to address this issue by developing an objective classification model capable of identifying different asphalt pavement surfaces with a high degree of accuracy using only field texture data. High-resolution texture data were collected on 21 highway sections with varying surface types within 60 mi of the city of Austin, Texas using a prototype developed in house. Sophisticated data-processing algorithms were created to ensure the data used in developing the classification model were of the highest possible quality. Based on the most recent literature on the topic, more than 20 texture summary statistics were assessed during this study to find the best combination to predict surface characteristics of asphalt pavements. The results are robust and indicate that texture summary statistics alone, when used in the right combination, have enough information to develop a pavement surface classification model with a predictive power as high as 89% based on the F1 score.