Scour depth prediction, using computer vision at a FlexiArch bridge

This research links the changes in arch displacement to the easement of support at the bridge foundation due to scour. Scour predictions are generally made using empirical equations which do not provide accurate estimations of scour and scour modelling has not reflected the true scour profile created by the fluid-structure interaction at individual bridges. Scale experiments and finite element modelling on a typical FlexiArch under loading with realistic simulated scour conditions and accurately modelled 3D scour, informed the development of a framework for computer vision-based monitoring (ArchIMEDES) and an artificial neural network (Keystone).

Scour depth and load carrying capacity predictions are made by Keystone for a monitored bridge based on the observed deformation in the arch ring extracted using ArchIMEDES. This contributes to the development of a low cost, non-intrusive sensor to identify scour development so it can be investigated and remediated before a bridge collapses. <br><br><i>Thesis is embargoed until 31 July 2030</i>