STANDARD METHODOLOGY FOR PUBLIC WORKS INSPECTION INTEGRATING DRONES AND ARTIFICIAL INTELLIGENCE

Abstract

The inspection of roads, bridges, dams, and buildings, is essential for ensuring the safety, compliance, and operational efficiency of public works. However, traditional inspection methods are often time-consuming, costly, and limited in accuracy, particularly in hard-to-access areas. The use of drones and artificial intelligence (AI) in  such inspections represents a significant advancement in terms of efficiency and precision. Yet, the lack of a standardized methodology limits the full adoption of these technologies. This study proposes and validates a standardized methodology for public works inspection, by integrating drones equipped with advanced photogrammetry technology and AI. The methodology leverages scale bars to enhance measurement accuracy and AI algorithms for automated diagnostics, enabling the precise identification of structural defects and anomalies. Case studies were conducted on urban pavement, buildings, bridges, and concrete dams, demonstrating the effectiveness of the proposed approach over a variety of infrastructure types and inspection scenarios. The results indicate high accuracy in measurements and in the identification of construction defects and pathological manifestations in the analyzed structures. This novel approach offers significant improvements in accuracy, safety, speed, and efficiency in public works management, while providinga replicable framework for public agencies and regulatory bodies worldwide.