Bridge deck surface damage assessment using point cloud data

Abstract Bridge deck condition assessments are typically conducted through visual inspections and by incorporating traditional contact sensors for Non-Destructive Evaluation techniques such as hammer sounding and chain dragging, which require the keen expertise of trained inspectors. The accuracy of these inspections is proportional to the level of deterioration of the bridge deck, as the ability of the inspectors is correlated to the apparent level of damage. This study aims to improve the accuracy of bridge deck inspection processes by utilizing non-destructive evaluation techniques, including analyzing point cloud data gathered via Light Detection and Ranging (LiDAR) as a geometry-capturing tool for identifying surface irregularities. This research aims to evaluate and quantify the effectiveness and efficiency of LiDAR sensors in contributing to the suite of technologies available to perform bridge deck condition assessment. To achieve this, the research proposes to understand the deterioration pattern of New Jersey bridges, evaluate the results gathered from point cloud data collected on a full-scale bridge deck, and quantify the information gained from deploying LiDAR on operating bridges in New Jersey. Two data processing approaches were chosen to measure the gross and fine dimensions of the evaluated bridge decks, such as the Curvature Extraction and Slope Analysis method, and the Least Square Plane Fitting method, resulting in an accuracy of 97.92% in reference to the results gathered from reports generated through the analysis of state-of-the-art NDE technology data and visual inspection.