A Strategic Framework for Integrating Unmanned Aerial Vehicles (UAV)-Based Pavement Assessments into the Egyptian Road Network

Abstract

Pavement condition assessment is essential for effective road network management, as paved roads deteriorate over time due to traffic loading and environmental effects. Traditional pavement surveys rely on in-situ measurements and visual inspections to identify surface distresses such as cracking, raveling, and weathering. Although widely used, these methods are often labour-intensive, time-consuming, costly, and may disrupt traffic while exposing inspectors to safety risks. Recent advances in unmanned aerial systems (UAS) provide a promising alternative for pavement condition assessment. UAV-based surveys enable rapid data collection over large areas using high-resolution imaging and sensor technologies, which can be integrated with artificial intelligence (AI) techniques for automated pavement distress detection and analysis. In Egypt, the rapid expansion of the road network and increasing maintenance demands highlight the need for an efficient, continuous, and reliable pavement monitoring system. This study presents an Egypt-focused framework that links UAV data acquisition, AI-based distress detection, and PCI-based decision-making to support the integration of UAV-based pavement inspection into existing road management practices. This study supports an Egypt-focused framework for integrating UAV-based pavement inspection into existing road management practices. The proposed framework outlines UAV data acquisition, AI-based distress detection, and pavement condition evaluation workflows, while considering local environmental, operational, and regulatory constraints. The framework is informed by successful international applications and is intended to enable safer, faster, and more cost-effective pavement assessment to support sustainable road network management in Egypt.