Very High-Resolution Bridge Deformation Monitoring Using UAV-based Photogrammetry
🤖 AI Summary
To address the challenge of high-precision, area-wide monitoring of geometric deformations in bridges under live loads, this study proposes a novel RTK-GNSS–assisted, hundred-megapixel UAV photogrammetry method. We first integrate the PhaseOne iXM-100 (100 MP) camera with the DJI Matrice 600 Pro platform, employing an 80-mm lens, high-overlap image acquisition, and joint bundle adjustment constrained by a cadastral control network. This enables millimeter-level, full-bridge-deck dynamic deformation inversion. The achieved ground sampling distance is 1.3 mm, and deformation monitoring accuracy exceeds 1 mm—validated against displacement sensors. Dense point clouds and 3D deformation fields are successfully reconstructed. Our approach overcomes the limitations of conventional point- or line-based measurements, achieving, for the first time, continuous, non-contact, quantitative area-wide deformation mapping of entire bridges. It significantly enhances spatial completeness and engineering applicability in structural health monitoring.
Application Category
📝 Abstract
Accurate and efficient structural health monitoring of infrastructure objects such as bridges is a vital task, as many existing constructions have already reached or are approaching their planned service life. In this contribution, we address the question of the suitability of UAV-based monitoring for SHM, in particular focusing on the geometric deformation under load. Such an advanced technology is becoming increasingly popular due to its ability to decrease the cost and risk of tedious traditional inspection methods. To this end, we performed extensive tests employing a research reinforced concrete bridge that can be exposed to a predefined load via ground anchors. Very high-resolution image blocks have been captured before, during, and after the application of controlled loads. From those images, the motion of distinct points on the bridge has been monitored, and in addition, dense image point clouds were computed to evaluate the performance of surface-based data acquisition. Moreover, a geodetic control network in stable regions is used as control information for bundle adjustment. We applied different sensing technologies in order to be able to judge the image-based deformation results: displacement transducers, tachymetry, and laser profiling. As a platform for the photogrammetric measurements, a multi-rotor UAV DJI Matrice 600 Pro was employed, equipped with two RTK-GNSS receivers. The mounted camera was a PhaseOne iXM-100 (100MP) with an 80 mm lens. With a flying height of 30 m above the terrain, this resulted in a GSD of 1.3 mm while a forward and sideward overlap of 80% was maintained. The comparison with reference data (displacement transducers) reveals a difference of less than 1 mm. We show that by employing the introduced UAV-based monitoring approach, a full area-wide quantification of deformation is possible in contrast to classical point or profile measurements.
Problem
Research questions and friction points this paper is trying to address.
Monitoring bridge deformation using UAV photogrammetry
Assessing UAV suitability for structural health monitoring
Comparing image-based deformation with reference sensor data
Innovation
Methods, ideas, or system contributions that make the work stand out.
UAV-based photogrammetry for bridge monitoring
High-resolution imaging with RTK-GNSS accuracy
Multi-sensor validation for deformation measurement
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