Leveraging local neighborhood features in 3D unstructured point cloud data for geometry-based automated damage delineation

Abstract

In a post-earthquake environment, manual inspection is resource-intensive and subjective. This article presents a real-world damage assessment study of the Improved Iterative Bi-Level Thresholding (IIBLT) algorithm. Damaged regions are delineated using IIBLT based on the local point curvature. The methodology is tested using a unique dataset: Mono Temporal Disaster City 3D Dataset (C3DO). C3DO is a point cloud data generated by a 3D reconstruction of a controlled post-earthquake environment, which presents challenges for concrete façade damage assessment algorithms. Results from IIBLT demonstrated a 16% increase in Matthews Correlation Coefficient (MCC) and Intersection over Union (mGIoU), compared to the current state-of-the-art methods. This corresponds to improving accuracy and completeness in defect region identification without the dependence on knowledge of damaged regions. Results from this study highlight the novel contributions of the IIBLT algorithm including the elimination of parameter tuning, and demonstrate the applicability of the novel iterative framework for concrete surface defect delineation at large.