Thermal Profiling of Asphalt Pavement Construction Using Unmanned Aerial Vehicle

Abstract

The in-place density of asphalt pavements is a key indicator of construction quality, durability, and long-term performance. Differential thermal readings—also known as thermal segregation—and lack of uniformity in mat temperature were identified as key factors in achieving target densities. A protocol was developed in this study to identify and quantify temperature differentials using an unmanned aerial vehicle (UAV) equipped with a thermal sensor. Eight sites were visited to gather thermal data of paving construction ranging from 0 min up to 60 min after the pavement has been placed. A custom-developed Python script was developed to quantify temperature differentials and analyze the data in three sections: 1) identifying and visually quantifying thermal differentials, 2) detecting the non-uniform mat temperatures such as locally segregated spots, and 3) analyzing the cooling pattern of different sites as a function of time. Validation metrics such as the Gini index, percent of non-uniformity, and coefficient of variation were computed in discussing each of the sites’ thermal profiles. The UAV system not only provided the ability to increase spatial coverage but also gave the option to monitor the sublots within the window of compaction. These features were not possible with the existing thermal scanning products. Various temperature anomalies were identified and quantified in the sites visited, including local segregated spots, longitudinal center of lane streaks associated with gearbox and/or chain case segregation, and V-shaped pattern of cold spots indicator of paver stop-and-go operation.