Original scientific paper
https://doi.org/10.20532/cit.2024.1005830
A Crack Detection Method for Civil Engineering Bridges Based on Feature Extraction and Parametric Modeling of Point Cloud Data
Yinlong Li
; NanChong Vocational and Technical College, Department of Civil and Architectural Engineering, Sichuan, China
*
Maoyao Li
; NanChong Vocational and Technical College, Department of Civil and Architectural Engineering, Sichuan, China
Hui Tang
; NanChong Vocational and Technical College, Department of Civil and Architectural Engineering, Sichuan, China
* Corresponding author.
Abstract
Accurate detection and analysis of cracks is critical for ensuring the safety and reliability of concrete bridges. Point cloud data (PCD) obtained from 3D scanning provides a promising avenue for automated crack assessment. However, processing the massive and unstructured PCD poses significant challenges in feature extraction and crack modeling. This paper proposes a novel method for bridge crack analysis by combining PCD feature extraction with a hierarchical neural network and Rodriguez rotation. The method first extracts crack features from PCD using outlier removal, denoising, and 3D coordinate conversion. A crack analysis model is then constructed by integrating multi-scale feature extraction and Rodriguez rotation into a hierarchical neural network, enabling the capture of both local and global crack patterns. Experiments on a benchmark data set demonstrate the effectiveness of the proposed approach, achieving 92.83% feature extraction accuracy, 95.73% parameter analysis accuracy, 93.51% recognition accuracy, and 0.91 F1 score. The method also shows improved efficiency compared to existing techniques. These results highlight the potential of the proposed PCD-based approach for accurate and efficient crack analysis in concrete bridges.
Keywords
Point cloud data; Bridge engineering; Cracks; Layered neural network; Analysis model
Hrčak ID:
321565
URI
Publication date:
30.9.2024.
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