Rapid Assessment of Dynamic Friction Coefficient of Asphalt Pavement Using Reflectance Spectroscopy


Mapping road conditions is a vital issue for city and state authorities worldwide. Today, pavement safety is assessed by specific assemblies primarily based on a mechanical wheel device, which is a methodology that's limited in its potential product and operation. In this letter, we examined the possibility of harnessing remote reflectance spectroscopy to predict asphalt's dynamic friction coefficient, thereby enabling the identification and mapping of road conditions. We used a near-infrared analysis technique to guage an artificial neural network prediction model designed to assess the friction coefficient solely from spectral readings. This letter describes the method for extracting such a model and presents promising results with an accuracy of and high significance of between actual and predicted friction values. This model was acquired using nine principal elements and three neurons. The potential of this technology is also discussed.

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