Airport Pavement Inspection Through Multi-Static GPR Surveys Using Deep Neural Networks

A novel method for airport pavement inspection was proposed, combining multi-static Ground Penetrating Radar (GPR) data with deep learning techniques. The study utilized the Markov Transition Field (MTF) to transform GPR time-series data into two-dimensional images suitable for analysis by Convolutional Neural Networks (CNNs). This framework enabled the automatic detection and classification of interlayer debonding in pavements. Experimental validation was conducted using real-world data from Haneda International Airport, demonstrating the capability of the proposed method to accurately identify debonding regions. The results highlighted the framework's efficiency and reliability in monitoring airport pavements, making it a promising tool for infrastructure maintenance and management.