Intelligent pavement condition survey: Overview of current researches and practices

Abstract

Automated pavement condition survey is of critical importance to road network management. There are three primary tasks involved in pavement condition surveys, namely data collection, data processing and condition evaluation. Artificial intelligence (AI) has achieved many breakthroughs in almost every aspect of modern technology over the past decade, and undoubtedly offers a more robust approach to automated pavement condition survey. This article aims to provide a comprehensive review on data collection systems, data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey. In particular, the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles. The AI-driven hardware devices including right-of-way (ROW) cameras, ground penetrating radar (GPR) devices, light detection and ranging (LiDAR) devices, and advanced laser imaging systems, etc. These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement. In addition, this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses, measuring pavement roughness, identifying pavement rutting, analyzing skid resistance and evaluating structural strength of pavements. Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies, remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

Keywords

• Pavement condition survey
• Pavement data collection
• Artificial intelligence
• Machine learning
• Deep learning
• Pavement condition evaluation