Non-Temporal Point Cloud Analysis for Surface Damage in Civil Structures

Abstract

Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.

Feature-Based Point Cloud-Based Assessment of Heritage Structures for Nondestructive and Noncontact Surface Damage Detection

“…The point cloud damage detection method presented in this study is proposed by Mohammadi et al [9]. Within the study, the authors proposed a damage detection workflow based on a pattern recognition approach and used only local geometric features to identify the damaged points with a point cloud irrespective of input point clouds underlying geometry.…”

“…Mohammadi et al proposed workflow starts by regularizing the point-to-point spacing (i.e., point density or point cloud resolution) within the point cloud data through a voxelating process where a representative centroidal point is computed and used to represent all the points within a voxel [9]. Afterward, the sparse and erroneous points are eliminated through a statistical outlier removal [32].…”

“…Due to the unstructured nature of the point clouds, these data representations are initially used for tasks such as measurement and visualizations. As a result, multiple methodologies have been introduced to process and analyze point clouds for damage detection by extracting and exploiting various features such as local geometric variations of points, color, or intensity value variation of the points, as well as comparing temporal datasets for change detection analysis [5][6][7][8][9].…”

“…The analyzed wall segment is located in a room at the southeast corner structure, known as Sala degli Elementi. The analyzed wall segment sustained significant cracking on its interior walls, where a single point cloud data of the wall segment is processed using an extension of the method introduced by Mohammadi et al [9]. Single point cloud data here emphasizes that the analysis is performed without relying on comparison between two or more datasets collected at different times.…”

Nonlinear dynamic tests of a reinforced concrete frame building at different damage levels

“…The collected lidar data were used to investigate the evolution of damage due to the forced-vibration tests in a two-step analysis process, namely surface defect detection and quantification of detected defects. 30,31 The damage features on the F I G U R E 2 0 Acceleration configuration plots during a sine step excitation in the Y direction at DS1 RC columns and masonry infills of the west side of the second story are summarized in Tables 11 and 12, respectively. This side was the mostly damaged side of the structure, as well as the side with better access to obtain the scans.…”