TLS-Based Feature Extraction and 3D Modeling for Arch Structures

Abstract

Terrestrial laser scanning (TLS) technology is one of the most efficient and accurate tools for 3D measurement which can reveal surface-based characteristics of objects with the aid of computer vision and programming. Thus, it plays an increasingly important role in deformation monitoring and analysis. Automatic data extraction and high efficiency and accuracy modeling from scattered point clouds are challenging issues during the TLS data processing. This paper presents a data extraction method considering the partial and statistical distribution of the point clouds scanned, called the window-neighborhood method. Based on the point clouds extracted, 3D modeling of the boundary of an arched structure was carried out. The ideal modeling strategy should be fast, accurate, and less complex regarding its application to large amounts of data. The paper discusses the accuracy of fittings in four cases between whole curve, segmentation, polynomial, and B-spline. A similar number of parameters was set for polynomial and B-spline because the number of unknown parameters is essential for the accuracy of the fittings. The uncertainties of the scanned raw point clouds and the modeling are discussed. This process is considered a prerequisite step for 3D deformation analysis with TLS.

Finite Element Analysis based on A Parametric Model by Approximating Point Clouds

“…In the south surface example, numbers of control points are 36 in the height direction and 165 in the length direction. The accuracy characteristic of B-spline surfaces in [6,63,67] is proved which adopts 20 control points in the approximation progress regarding an arch structure with the length of 2 m, which indicated that there is a control point every 0.1 m. The determination regarding the quantity of control points affects the quality of the approximated surface. Detailed explanations regarding the parameter and model selection problem are given in [63].…”

“…The convention of this special situation is to default this as 0 in the recursion work. For detailed explanations, please refer to [12,67].…”

“…The first B-spline approximation task is based on curves which are of significance to describe different feature boundaries of the structure, for example, windows and doors. The degree value of basis functions is 3 [6,67]. The values of the control point number of different feature boundaries regarding the east side wall in Figure 9 are listed in Table 1.…”

Robust model reconstruction for intelligent health monitoring of tunnel structures

“…[15][16][17][18][19][20][21][22] The TLS combined with robotic systems have great prospects, especially for application in the area of intelligent health monitoring. [23][24][25][26][27][28][29] This article is structured as follows: the first section gives an introduction of the research background and motivation; the second section presents the mathematical methods of robust modeling using B-spline surface; the third section describes the details of data used in this study, including simulated data and tunnel measurement data; the fourth and fifth sections show the analysis results of the simulated and measurement data; lastly, the sixth section draws conclusion of the study.…”

Intelligent crack extraction and analysis for tunnel structures with terrestrial laser scanning measurement

“…As terrestrial laser scanners have become more available, their applications have become more widespread. [16][17][18][19][20][21][22][23][24][25][26][27][28] Unfolding and projection of the point cloud A framework of crack extraction and analysis is presented in Figure 1 involving five steps. First, the 3D data of tunnel structures was collected utilizing TLS technology; second, we obtained the point cloud projection and transferred the point cloud data to an image file; third, we carried out image preprocessing; fourth, the automatic crack identification was programmed; and, finally, we analyzed the cracks of the tunnels.…”