Towards an AI-based early warning system for bridge scour

Abstract

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 programme. 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.