Reconstruction of As-built Civil Infrastructure using LiDAR to Support Digital Twin Visualization
Date of Award
Master of Science
Digital twin (DT) technology is state-of-the-art for engineering processes using a virtual model of a physical system with near real-time data exchange (either fully or semi-automated) during the process (collection and feedback). The potential of DT for as-built civil infrastructure remains relatively unexplored. To achieve the expectation of a DT, three main capacities must be met: (1) accurate virtual twin of physical asset (2) continuous data acquisition and data management (3) automated or semi-automated decision making from the data exchange. The challenge addressed herein is to determine a feasible approach to create an accurate virtual model (efficiently) from light detection and ranging (LiDAR) data for existing infrastructure. The scope of this research focuses on the first capacity by comparing various virtual twin model creating methods for visualization of an existing pedestrian bridge, determining compatibility with data input, and proposing a data structure for predicting hidden structural components not captured by LiDAR. The initial attempts to improve infrastructure management practices by leveraging a DT for various applications were investigated in the literature review. The methods investigated were direct, manual conversion from scan to Building Information Modeling (BIM), and algorithm-based reconstruction techniques via an open-source software. In all cases, it was found that the end results were short of the requirements to integrate into a DT, and that substantial human input was still required. A potential for closing the gap between these technologies and DT was identified via automated approaches like machine learning or artificial intelligence, but this would require substantial amounts of data that is not yet available.
Received from ProQuest
Jose Luis Lugo
Lugo, Jose Luis, "Reconstruction of As-built Civil Infrastructure using LiDAR to Support Digital Twin Visualization" (2022). Open Access Theses & Dissertations. 3513.