Semi-automated Generation of Geometric Digital Twin for Bridge Based on Terrestrial Laser Scanning Data

Abstract

Digital twins (DTs) have a great potential for bridge operation and maintenance. Geometric digital twins (gDTs) are the key component of DTs. At present, a growing number of researchers are using high-precision 3D laser point clouds to generate gDTs. However, for large bridges, such as arch, cable-stayed, and suspension bridges, comprehensive point-cloud collection stations are difficult to set up due to their large span, narrow site, and limited field of vision. Consequently, the complete point clouds of these bridges cannot be easily obtained. Thus, knowing how to process absence point clouds and generate gDTs is an urgent problem. This study proposes a semiautomatic method of extracting geometric information of a bridge’s components in the absence of point clouds. First, an algorithm based on the combination of the iterative polynomial fitting curve and sliding window is developed to extract the arch ring accurately. Second, an improved random sample consensus (RANSAC) algorithm based on distribution density is adopted to extract the cross sections of the arch bridge components, except the arch ring. For cross sections that lack point clouds, a translation strategy is used to supplement the unknown line segment. Finally, for the T-beam, a model alignment method is proposed to best match the characteristic intersections extracted by the improved RANSAC algorithm and the points corresponding to the design model. The quality of the generated models is gauged using a point cloud deviation chromatogram. In addition, the stressed component piers are compared with its design parameters to verify the accuracy of the proposed method. Results show that our method can efficiently and accurately extract geometric information and generate gDT for the bridge.

Research on the development and application of digital twin for the full life cycle of bridge engineering

“…Also, Hu et al set up a semi-automated method to generate bridges without point clouds. It is done by a translation strategy to replenish the unknown, which performs well [23]. Moreover, Mohammadi proposed a methodology combining comprehensive judging criteria to evaluate the accuracy of Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicles (UAV).…”

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

“…Several studies used TLS to generate DTs of the bridges. For instance, Hu et al [34] proposed a method for 3D model generation using point cloud data acquired from TLS. Omer et al [47] also applied TLS for the purpose of constructing the DT model to examine the condition of concrete bridge structures and employed virtual reality (VR) for the inspection of bridges within an immersive 3D virtual environment.…”

“…Many methods in the literature define bridge DT as a FEM updating approach that collects data from different sensors, explores potential relationships between different datasets, considers uncertainties, and makes predictions for bridge safety and reliability in specific situations [28][29][30][31][32]. Other methods consider DTs as an as-is BrIM updating approach to generate a DT-enhanced BrIM framework with the latest inspection damage information [33][34][35][36]. These research studies can help in tackling the dilemma of changing from static and enclosed information with recurring compatibility difficulties to a connected data model, where the physical asset can be completely represented as a DT.…”

Research progress and prospect of digital twin in bridge engineering

“…However, the method still struggles with consistent boundary distinction between adjacent components. There are also works on how the geometric information of the bridge can be accurately extracted from the arch bridge with missing point cloud to generate the gDT (Hu et al, 2023). Photogrammetry has also been employed to create the gDT of bridges (Pantoja-Rosero et al, 2023; Zhou et al, 2022).…”