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Currently, the condition of masonry arch bridges is 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 geometry data of a structure is becoming increasingly fast and this trend will continue, especially with the use of drones. There is therefore a large opportunity to collect and use this data to automate the visual inspection process through digital means.
This presentation will look at ways which have been developed for processing the collected image and laser scan data to automatically locate and identify defects on the structure and build a damage model of the structure. This damage model is used to inform the condition assessment of the structure through identifying failure mechanisms.
Cambridge Centre for Smart Infrastructure and Construction, University of Cambridge
e: [email protected]