Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore

Management of ageing masonry arch bridges entails periodic site inspections to identify signs of potential structural degradation. Previous research has focused on detecting surface cracks from images. This paper develops an alternative approach where cracks are identified from point clouds via geometric distortions. An image-based anomaly detection method called PatchCore is customized for 3D applications for this purpose. First, Fast Point Feature Histograms (FPFH) are used to extract geometric features. Then PatchCore is applied on synthetic point clouds with crack labels, generated using 3D finite element modelling (FEM) and graphical modelling. Results show that the proposed method can capture surface and internal cracks in arches. Analyses show that the method is robust against measurement noise, initial damage and masonry surface roughness, and can be applied to other bridge components. Limitations of the method in detecting small changes in curvature and in-plane geometric distortions are highlighted for further improvements. • Detection of cracks from point clouds using geometric distortions. • Realistic masonry arch point clouds synthesized using finite element models and Blender. • Local geometric features characterized in multi-scale. • PatchCore is modified for anomaly detection in 3D point clouds. • Method enables detections of invisble cracks.