Feature Selection-Based Method for Scaffolding Assembly Quality Inspection Using Point Cloud Data

被引:2
|
作者
Zhao, Jie [1 ]
Chen, Junwei [2 ]
Liang, Yangze [1 ]
Xu, Zhao [1 ]
机构
[1] Southeast Univ, Dept Civil Engn, Nanjing 210096, Peoples R China
[2] China Railway Siyuan Survey & Design Grp Co Ltd, Wuhan 430063, Peoples R China
基金
中国国家自然科学基金;
关键词
scaffolding structure; point cloud; machine learning; feature select; genetic algorithm; 3D; BIM;
D O I
10.3390/buildings14082518
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes an automated method for scaffolding assembly quality inspection using point cloud data and feature selection algorithms. High-precision point cloud data of the scaffolding are captured by a Trimble X7 3D laser scanner. After registration with the forward design model, a 2D slicing comparison method is developed to measure geometric dimensions with an accuracy controlled within 0.1 mm. The collected data are used to build an SVM model for automated assembly quality inspection. To combat the curse of dimensionality associated with high-dimensional data, an optimized genetic algorithm is employed for the dimensionality reduction in the raw sample data, effectively eliminating data redundancy and significantly enhancing convergence speed and classification accuracy of the detection model. Case studies indicate that the proposed method can reduce feature dimensionality by 70% while simultaneously improving classification accuracy by 13.9%. The proposed method enables high-precision automated inspection of scaffolding assembly quality. By identifying the optimal feature subset, the method differentiates the priority of various structural parameters during inspection, providing insights for optimizing the quality inspection process.
引用
收藏
页数:20
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