Multi-lens pier scour monitoring and scour depth prediction

Abstract

Severe pier scour is usually the major cause of bridge failures. It is therefore crucial to develop various scour monitoring techniques and prediction models for real-time warning of bridge safety. In the present study, a multi-lens monitoring system for pier scour under laboratory conditions has been developed. By utilising a plurality of lenses, this system is capable of tracking scour images and obtaining real-time scour-depth variation through a series of image recognition processes. Laboratory experiments under unsteady flow conditions were carried out to validate the proposed monitoring method. Then, three time-dependent scour prediction models were employed for simulation and comparison with the measured data. In order to improve the scour prediction results, with the real-time scour monitoring data, a data assimilation scheme is proposed and applied to the scour model under clear-water scour conditions. The result shows that the accuracy of scour prediction for a lead time of 3 h can be improved significantly.