Methods for Asphalt Polymer Pavement Surface Monitoring: A Review

Abstract

Bitumen, also known as solid asphalt, is an intricate blend of organic molecules obtained during the distillation of crude oil. The chemical composition of it varies based on its origin, processing techniques, and intended use. Development in transportation sector led to increase the traffic volume in past year. Increase in the number of vehicles have tendency to deteriorate the pavement [paved or unpaved] surface due to heavy load and their repetitions. Heavy vehicle [>4 axle] has more tendency to damage the pavement, so pavement surface monitoring emerges to maintain the pavement surface. Pavement surface monitoring also helps in road safety also, as many accidents occur because of pavement distress like potholes, shoving, rutting etc. There are many methods for pavement surface monitoring, methods may be manual, and semi-manual. In this study semi-manual method is focused. In this method pavement distress images can be captured using high resolution camera of smart mobiles or tablets, in built with GPS. More than 5000 images will be captured of different types of distresses, all distresses are varied in sizes also, and of different location. These images are enhanced using MATLAB or any other software and then coded for deep learning. In deep learning method all the data set of pavement distresses are trained so that we can easily identify a new distress without any time delay. These images are also mapped on GeoSetter software, so that data set can be imported to Google earth file and that can be submitted to maintenance department for healing the pavement distress.

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