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基于强形式无网格法和基本边界条件捕捉的 LiDAR 点云数据组合结构分析。

LiDAR Point Cloud Data Combined Structural Analysis Based on Strong Form Meshless Method Using Essential Boundary Condition Capturing.

机构信息

Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.

Department of Civil Engineering, Myongji College, Seoul 03656, Republic of Korea.

出版信息

Sensors (Basel). 2023 Jun 30;23(13):6063. doi: 10.3390/s23136063.

DOI:10.3390/s23136063
PMID:37447913
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10346733/
Abstract

This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques.

摘要

本研究提出了一种新颖的混合仿真技术，用于分析结构变形和应力，使用光探测和测距 (LiDAR) 扫描点云数据 (PCD) 和多项式回归处理。该方法从 PCD 中估计变形结构的边缘和角点。它将其转换为粒子差分法 (PDM) 的狄利克雷边界条件，该方法仅基于强形式使用节点，有利于处理本质边界和节点重新排列，包括分析步骤之间的节点生成和删除。与以前依赖于附有目标的数字图像的研究不同，本研究使用 LiDAR 扫描在加载过程中获取的 PCD，而无需任何目标。本质边界条件的实现自然为 PDM 模拟构建了边值问题。通过弹性梁问题和橡胶梁的三点弯曲测试验证了所开发的混合仿真技术。结果与 ANSYS 分析进行了比较，表明该技术准确逼近了变形边缘形状，从而实现了准确的应力计算。当使用线性应变模型和增加 PDM 模型节点数量时，精度会提高。此外，PCD 处理和边缘点提取过程中的误差受到多项式回归方程阶数的影响。该仿真技术在将数值分析与数字图像联系起来具有挑战性且直接进行机械测量困难的情况下具有优势。此外，它在涉及机器引导技术的结构健康监测和智能施工中有潜在的应用。

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