Bridge scour monitoring and forecast: implementation of an AI early warning system
Project Leader: Negin Yousefpour
Collaborators: USGS, US Department of Transport
Sponsors: Arup
Primary Contact: Negin Yousefpour (negin.yousefpour@unimelb.edu.au)
Keywords: artificial intelligence
Disciplines: Infrastructure Engineering
Domains:
Bridge scour has been a challenge across the world (accounts for 60% of bridge collapses in the US for example). Throughout the past decade, remote scour monitoring programs have been devised and implemented by transport authorities to facilitate bridge management. This research aims to develop cutting edge AI/ML algorithms based on real-time scour monitoring data to generate early warning systems to forecast scour depth at bridge piers.
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