Towards an AI-based early warning system for bridge scour

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