ADVANCING BATHYMETRY AND STREAMFLOW SURVEY TO REAL-TIME SCOUR PREDICTION: AN AUTOMATED ALGORITHM

Most research on scour assessment has traditionally focused on developing best-fit or envelope equations to estimate the maximum scour depths from physical experiments in a laboratory environment. However, obtaining an accurate estimate is always a challenge due to numerous uncertainties in the actual riverine or coastal environment. With advances in new surveying technologies and growing computational power, a promising alternative approach is to develop a robust high-speed computational fluid dynamics (CFD) model to predict scour depths, final bathymetries and further assess scour risks during flood events in a real-time fashion.To achieve this approach, the first step is to rapidly collect bathymetry in point cloud format, convert to a surface mesh, integrate streamflow data, and generate a robust CFD model for simulating flow conditions. The collection of the bathymetric data and processing them to a precise CFD model must be efficiently and effectively performed by advanced mathematical algorithms for this approach to be viable. Therefore, the objective of this project is to develop a sophisticated algorithm to automatically transfer real-time bathymetry and streamflow data to an accurate CFD model for scour prediction during storm events.