Spectral modelling used to identify the aggregates index of asphalted surfaces and sensitivity analysis

• Definition of Exposed Aggregate Index (EAI) by classification of pictures.
• Investigation asphalt’s spectral characteristics.
• Empirical relation among spectral and pictures data was obtained.
• Results allowed to test robustness of equation for retrieval of EAI.

This article focuses on the spectral variability of asphalt compared with its surface characteristics, in particular, with the amount of exposed aggregate. Any uncertainties in the chemical or physical composition of the asphalt or in its alteration status may cause erroneous results. Improved understanding of the spectral properties of asphalt can be used to optimise road network management policies. Pavement aging and degradation detection is one of the primary issues in infrastructure management faced by local authorities in the area of safety standards. In this study, various asphalt samples from Central Italy are characterised by digital RGB photos combined with their spectral signatures. Because the spectral response is influenced by the presence of exposed aggregate and bitumen, it is crucial to define an objective index that could indicate either their presence or absence. Using the Exposed Aggregates Index (EAI) method on each photo, the aggregates surface occupation is determined by the supervised classification of the RGB photo dataset using the parallelepiped method. The result is then compared with a spectral response of the target. Using this process, a series of new spectral indices is identified in a range of wavelengths from 400 to 900 nm that show statistical correlation and physical significance to changes in bitumen and exposed aggregates. In particular, the first derivative of the spectrum at 400 nm and the reflectance values at 460, 490, 740 and 830 nm are very sensitive to changes in the EAI. An empirical relation for the exposed aggregates is found during the calibration step for any of the relations between the spectral index and the EAI. This relation is linked to the degradation of the targets with an RMSE of 0.09. The final phase of the work focuses on uncertainty and sensitivity analyses of the model, demonstrating the robustness of the equation identified for the relation.

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