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3D Reconstruction and Measurement of Surface Defects in Prefabricated Elements Using Point Clouds

点云 激光扫描 过程(计算) 计算机科学 质量(理念) 数据挖掘 曲面重建 点(几何) 逆向工程 控制(管理) 工程类 还原(数学) 质量管理 曲面(拓扑) 计算机视觉 工程制图 德劳内三角测量 数据质量 生产(经济) 自动化 数据缩减 预制 过程控制 等高线 人工智能 数据收集 建筑工程 详细程度 控制点 施工管理 激光器
作者
Zhao Xu,Rui Kang,Ruodan Lu
出处
期刊:Journal of Computing in Civil Engineering [American Society of Civil Engineers]
卷期号:34 (5) 被引量:83
标识
DOI:10.1061/(asce)cp.1943-5487.0000920
摘要

Due to a higher efficiency and lower cost, prefabricated construction is gradually gaining acceptance within the market. Laser scanning has already been adopted in civil engineering to reconstruct a three-dimensional (3D) model of a structure, to monitor the deformation, and so on. This paper seeks to explore a more automated and accurate quality control process, focusing on the surface defects in prefabricated elements. Laser scanning is adopted for data collection and the 3D reconstruction of the prefabricated components. Besides, a new point cloud preprocessing, involving the K-nearest neighbors (KNN) algorithm, a reduction of the data dimension, and data gridding, is developed to improve the efficiency and accuracy of subsequent algorithms. The Delaunay triangle is used to extract the contour of the point cloud, and then the contour is fitted to further determine the geometric data. Meanwhile, a comprehensive quality control system of prefabricated components based on relevant specifications is proposed, and the quality of prefabricated components is monitored intuitively by the values of indicators. In order to integrate it into the building information modeling (BIM) platform and better store the obtained quality information, the production quality information is designed to be extended to the Industry Foundation Classes (IFC) standard. The proposed approach will be applied to analyze the causes of quality problems in the production process and strengthen the quality control. This study designs a more efficient and accurate quality evaluation process, including data collection, data processing, indicator calculation, and quality evaluation. Moreover, the results moving forward can provide feedback to the cause of the quality issues and further improve the production quality of prefabricated elements.
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