  Scour is the number one cause of bridge failure in many parts of the world.
Considering the lack of reliability in existing empirical equations for scour
depth estimation and the complexity and uncertainty of scour as a physical
phenomenon, it is essential to develop more reliable solutions for scour risk
assessment. This study introduces a novel AI approach for early forecast of
scour based on real-time monitoring data obtained from sonar and stage sensors
installed at bridge piers. Long-short Term Memory networks (LSTMs), a prominent
Deep Learning algorithm successfully used for time-series forecasting in other
fields, were developed and trained using river stage and bed elevation readings
for more than 11 years obtained from Alaska scour monitoring program. The
capability of the AI models in scour prediction is shown for three case-study
bridges. Results show that LSTMs can capture the temporal and seasonal patterns
of both flow and river bed variations around bridge piers, through cycles of
scour and filling and can provide reasonable predictions of upcoming scour
depth as early as seven days in advance. It is expected that the proposed
solution can be implemented by transportation authorities for development of
emerging AI-based early warning systems, enabling superior bridge scour
management.
