Bridge deck disease detection and screening method based on point cloud Gaussian curvature field

In order to efficiently and automatically detect and classify bridge deck diseases, a method using Gaussian curvature to detect bridge deck surface diseases is proposed. When there is a defect in the bridge paving surface, there is a high-order discontinuity in the common boundary between the defect area and the non-defect area, that is, the surface Gaussian curvature value at the common boundary is a discontinuous change. According to the location and extent of Gaussian curvature mutation, the disease was located and quantified, and the disease was classified and screened by the Gaussian curvature value distribution feature and Hu rectangular morphological feature. For a 3D point cloud model of a concrete continuous girder bridge, the distribution of Gaussian curvature values in the paved area of the bridge deck was analyzed. The results show that the distribution of Gaussian curvature can characterize surface diseases such as pavement pits and loose spalling of the bridge, and this method is consistent with the assessment results of disease degree by traditional detection methods, and has high implementability, which provides a new technical approach for bridge deck disease assessment.