Evaluation of the Performance Capability of Remote Visual Inspection of Concrete Structures Using Drones

Close visual inspection (CVI) forms a cornerstone of asset integrity. Advances in access technologies, including drones, have led to their increased use for remote visual inspection (RVI). However, comparative studies of RVI and CVI, in terms of defect detection, are currently limited. In this study, controlled trials were conducted with multiple industrial participants operating drones to inspect a concrete block wall containing representative defects. RVI performance was assessed in terms of defect detection, identification and sizing. RVI demonstrated moderate performance, with an overall defect detection rate of approximately 50% and no participant exceeding 0.6. Detection was strongly dependent on defect type, with larger defects such as spalling and chipping consistently identified, while finer defects such as cracking were frequently missed. Identification of defect type was less reliable and influenced by inspector experience, while sizing capability was limited, with only one participant providing approximate measurements for larger defects. An automated visual inspection device, termed ALICS (Adaptive Lighting for the Inspection of Concrete Structures), was deployed on two samples. Images were captured of the concrete surface under varying lighting conditions to enhance the visibility of any present defects. Images were then analysed using artificial intelligence (AI), with the device identifying all defects in the tested areas. These results highlight both the current limitations of RVI and the potential of illumination-enhanced automated approaches to improve inspection reliability.