Spatial Interpolation of Bridge Scour Point Cloud Data Using Ordinary Kriging Method

Scour is a critical condition change for a bridge hydraulic system, and terrestrial light detection and ranging (LiDAR) scans have been suggested as a way to quantify the scour conditions. With LiDAR point cloud data, a temporal record of scour can be established. However, there are limitations to LiDAR scans. For example, laser light does not bend and can be obstructed by objects along the light path, resulting in missing geometric information behind the obstacles, thereby creating a void in the point cloud data. To “fill in” the missing data, spatial interpolation of three-dimensional (3D) LiDAR point cloud data using ordinary kriging (OK) is suggested, and actual field data from scanning three scoured bridge piers is presented to demonstrate the application. Kriging is a geostatistical interpolation technique and OK assumes that the spatial variation of the phenomenon or object being considered is random and intrinsically stationary with a constant mean. Here, the complete scour envelope is reconstructed using OK and is shown to have excellent results.