Mapping Mortar Joints in Image Textured 3D Models to Enable Automatic Damage Detection of Masonry Arch Bridges

Currently, the condition of masonry arch bridges are predominantly assessed via manual visual inspection. This process carries risk and cost due to the need for an inspection engineer to access sites in the proximity of busy... [ view full abstract ]

Currently, the condition of masonry arch bridges are predominantly assessed via manual visual inspection. This process carries risk and cost due to the need for an inspection engineer to access sites in the proximity of busy railway lines and roads. Manual visual inspection is also known to be subjective, relying on the inspection engineer’s interpretation of the structure’s condition. The collection of image and laser scan data is becoming increasingly fast (and this will continue with the use of drones for this purpose). There is therefore a large opportunity to collect and use this data to automate the visual inspection process through digital means.

Masonry surfaces, particularly on older structures, often contain defects (e.g. discoloration, cracks), which can provide evidence of damage. However, mortar joints also create a non-homogenous surface, which can make defect detection more difficult. Therefore, as a precursor to defect detection, a methodology has been developed to filter the individual bricks out from images of masonry, enabling defect detection within the bricks themselves, on the more homogeneous brick surface. This paper demonstrates a deterministic approach, first detecting the position of mortar joints in images, then determining the brick pattern to iteratively improve the automated detection by removing false mortar joints and adding undetected mortar joints. The methodology has been tested on a variety of brick masonry surfaces, and the success rate evaluated. Defect detection is then exemplified by applying a preliminary edge-detection-based defect detection process on masonry images with masked mortar joints.