Quality Evaluation of Digital Twins Generated Based on UAV Photogrammetry and TLS: Bridge Case Study

TLDR

Remote sensing technologies such as UAV photogrammetry and terrestrial laser scanning enable detailed virtual inspections of infrastructure, creating digital twins that offer safer, cheaper, and more reliable bridge assessments. This study proposes a comprehensive methodology to evaluate the quality and geometric accuracy of digital point clouds generated from UAV photogrammetry and TLS for an Australian heritage bridge. The methodology compares the two point clouds on metrics including point distribution, outlier noise, data completeness, surface deviation, and geometric accuracy, using a range of proposed approaches. The case study demonstrates the method’s effectiveness, showing that TLS-based point clouds achieve higher point density and better agreement with as‑is measurements than UAV-based clouds.

Abstract

In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known as the digital twins. Digital twins are suitable alternatives to in-person and on-site based assessments that can provide safer, cheaper, more reliable, and less distributive bridge inspections. In the case of bridge monitoring, Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are among the most common advanced technologies that hold the potential to provide qualitative digital models; however, the research is still lacking a reliable methodology to evaluate the generated point clouds in terms of quality and geometric accuracy for a bridge size case study. Therefore, this paper aims to provide a comprehensive methodology along with a thorough bridge case study to evaluate two digital point clouds developed from an existing Australian heritage bridge via both UAV-based photogrammetry and TLS. In this regard, a range of proposed approaches were employed to compare point clouds in terms of points’ distribution, level of outlier noise, data completeness, surface deviation, and geometric accuracy. The comparative results of this case study not only proved the capability and applicability of the proposed methodology and approaches in evaluating these two voluminous point clouds, but they also exhibited a higher level of point density and more acceptable agreements with as-is measurements in TLS-based point clouds subjected to the implementation of a precise data capture and a 3D reconstruction model.