Study on Representative Parameters of Reverse Engineering for Maintenance of Ballasted Tracks

Abstract

Reverse engineering (RE) is a technology used to create three-dimensional (3D) models by scanning structures and can be used to examine the current condition of structures. Applying RE to the maintenance of railroad facilities with a high proportion of safety accidents can be an alternative to increase the efficiency of railroad facilities. However, most tasks while constructing Building Information Modeling (BIM) after 3D scanning and extracting two-dimensional (2D) drawings are still performed manually. In particular, denoising, registration, and 3D modeling based on point clouds are labor-intensive and time-consuming tasks, and their efficiency needs to be enhanced by introducing automation technology. In this study, we selected point clouds-based representative parameters for ballasted tracks of a straight single-line section for automating railroad maintenance. Scan data and a BIM of a ballasted track were compared using the selected representative parameters. In addition, the types of damage to ballasted track requiring maintenance were examined. And a testbed was consisted of ballasted a track was selected, and 3D scanning was performed to obtain point cloud data of a testbed. Then, a BIM model was created by measuring the numerical values corresponding to the representative parameters on the scan data. The feasibility of constructing a railroad maintenance BIM based on representative 3D object detection parameters during RE work on the ballasted track was evaluated.

Systematic Literature Review of Open Infrastructure BIM

“…As far as railways are concerned, there are several studies testing the suitability of Open I-BIM for the planning, design, operation, and maintenance of railway works. Park et al [228] focused on reverse engineering a ballasted track of a straight railway stretch. The authors focused on methods for automatizing onerous and error-prone manual operations such as point cloud denoising and registration and 3D modeling, within BIM-based tools.…”

3D Point Cloud Dataset of Heavy Construction Equipment

“…Previous construction automation research has primarily focused on hardware advancements, such as highway road surface crack inspection and sealing equipment [6,7], bricklaying robots [8], autonomous robotic excavation for dry stone construction [9], and machine guidance (MG) and machine control (MC) for excavators [10,11]. However, the rapid development of Building Information Modeling (BIM) and Artificial Intelligence (AI) technologies has resulted in a significant increase in software-centric construction automation research [12][13][14][15][16][17][18][19]. Furthermore, the technological progress of AI-equipped autonomous vehicles and various sensors has paved the way for AI technology applications in construction, particularly object recognition research [20].…”