Quantifying Raveling Using 3D Technology with Loss of Aggregates as a New Performance Indicator

Abstract

Pavement raveling is one of the predominant distresses in the United States that impacts roadway safety and driver comfort on open-graded friction course (OGFC) pavements. Raveling specific treatments, such as fog seal and micro-milling the OGFC layer, can prolong pavement life and reduce resurfacing costs and environmental impact. However, with the current qualitative condition assessment methods (which rate pavements at Severity Levels 1–3 or as light, moderate, or severe), it is difficult to determine the optimal timing for these raveling treatments to be most effective. Therefore, there is an urgent need to develop a method to quantitatively evaluate the raveling condition. While 3D pavement technology provides opportunities for quantifying pavement raveling conditions using 3D pavement surface data, there are two main challenges for quantifying pavement raveling: (1) estimating a reference surface that represents the pavement without any raveling so that the actual pavement can be compared to the reference surface to quantify the raveling, and (2) obtaining pavement images with quantified raveling conditions (aggregate loss volume) for validation. This paper proposes a method with the loss of aggregate as a new performance indicator to automatically quantify raveling using 3D pavement surface data already collected by transportation agencies for pavement evaluation. The proposed method is validated using pavement images (with known aggregate loss) from simulated pavement mats fabricated in the lab and synthetic pavement images obtained by procedural generation. The proposed method consists of (1) 3D data acquisition; (2) pre-processing with (a) outlier removal and image smoothing, (b) two-sensor image stitching, and (c) range image rectification; (3) raveling detection using (a) region of interest selection, (b) reference surface estimation, (c) potential aggregate loss identification, and (d) noise removal; and (4) aggregate loss quantification. The validation results show a strong correlation (R = 0.99) between the computed aggregate loss and the expected aggregate loss. Better performance was observed with the proposed method than with other methods (such as the watershed method and the model fitting method). The proposed method provides a cost-effective means to quantify the loss of aggregates in support of quantitative raveling condition forecasting by leveraging 3D pavement data already collected by transportation agencies.