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 structures 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.

However, Masonry surfaces are often rough, and the grout-line patterns create a non-homogenous surface, making defect detection harder. Therefore, as a precursor to defect detection, a methodology has been developed to filter the individual bricks out from masonry images, enabling defect detection on the more homogeneous brick surface. This uses a deterministic approach, first detecting the position of grout-lines, and from these positions determining the brick spacing pattern, to then remove false grout-lines and add undetected grout-lines. The methodology has then been tested by applying a rudimentary crack detection process on masonry images with the grout lines masked. This applies a simple edge detection procedure and uses the geometry of detected points relative to one another to filter out genuine cracks from background noise.