Post‐earthquake structural damage identification and safety evaluation using point clouds

Abstract

Efficient and accurate post‐earthquake damage assessment of building structures is critical for ensuring the human safety and structural integrity of affected buildings. However, manual inspections and traditional visual damage identification methods are often constrained by the inaccessibility of hard‐to‐reach regions and the subjectivity of human inspectors. To overcome these issues, this paper proposes a novel approach for rapid and precise post‐earthquake damage identification and evaluation using unmanned aerial vehicle (UAV) and point cloud techniques, significantly reducing the time, labor, and errors associated with traditional methods. A comprehensive testing on full‐scale reinforced concrete shear walls was conducted to validate the precision and feasibility of this method. The point cloud models of the shear wall were generated leveraging UAV imagery and laser scanning technology with millimeter‐scale accuracy. The proposed algorithm effectively segmented each target plane of the shear wall, achieving a relatively satisfactory overall Intersection over Union of 99.25%. The relative errors of deformation between the algorithm's identification and gauges measurements were within 5%. This study successfully segmented and quantified structural surface damage, including cracks and spalling. Finally, the structural safety of the shear wall was evaluated according to ATC‐20 guidelines, using indicators such as inclination, story drift ratio, crack width, and damage area. Furthermore, proposed method was also verified in real cases.