Remote and rapid evaluation of steel bridge paint condition using wall-crawling drone integrated with laser thermography

Abstract

This study presents the development of a paint condition evaluation (PCE) system that integrates a wall-crawling drone with a laser thermography module for the remote and rapid inspection of steel bridge paint conditions. A wall-crawling drone was employed to access hard-to-reach areas for inspection. The drone crawled along the inspection surface when it arrived at the target inspection area. Simultaneously, the laser thermography module heats the target surface using a laser beam, and the resulting heat responses are recorded using an infrared (IR) camera. The recorded heat responses were analyzed using the PCE algorithm developed in this study, enabling visualization and quantification of the paint thickness distribution and hidden delamination. The key contributions of this study include (1) the integration of laser thermography with a drone for rapid and remote PCE, (2) inspection of inaccessible areas within a bridge structure using a drone with motorized wheels, and (3) automated quantification and visualization of invisible paint thickness distribution and hidden delamination. The performance of the PCE system was validated through lab-scale and field-bridge tests, demonstrating the successful visualization of the paint thickness and delamination area. The quantification error was less than 10 μm for paint thickness and 1 cm² for delamination area.