# Extraction of Corrosion Damage Features of Serviced Cable Based on Three-Dimensional Point Cloud Technology

## TL;DR

This paper uses 3D scanning to study corrosion damage on steel wires in bridges and identifies statistical patterns in the corrosion features.

## Contribution

The study introduces a novel method for extracting and statistically modeling corrosion pit features using 3D point cloud data.

## Key findings

- The directional angle of corrosion pits does not follow a known probability distribution.
- Pit width and defect parameter follow a generalized extreme value distribution.
- Pit length and depth are best described by a Gaussian mixture model.

## Abstract

The corrosion of high-strength steel wires is a key factor impacting the durability and reliability of cable-stayed bridges. In this study, the corrosion pit features on a high-strength steel wire, which had been in service for 27 years, were extracted and modeled using three-dimensional point cloud data obtained through 3D surface scanning. The Otsu method was applied for image binarization, and each corrosion pit was geometrically represented as an ellipse. Key pit parameters—including length, width, depth, aspect ratio, and a defect parameter—were statistically analyzed. Results of the Kolmogorov–Smirnov (K–S) test at a 95% confidence level indicated that the directional angle component (θ) did not conform to any known probability distribution. In contrast, the pit width (b) and defect parameter (Φ) followed a generalized extreme value distribution, the aspect ratio (b/a) matched a Beta distribution, and both the pit length (a) and depth (d) were best described by a Gaussian mixture model. The obtained results provide valuable reference for assessing the stress state, in-service performance, and predicted remaining service life of operational stay cables.