Remote Inspection of Bridges with the Integration of Scanning Total Station and Unmanned Aerial Vehicle Data
Year: 2024
Type: article
Abstract: Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS’s measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV’s measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV’s inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS’s high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV’s efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition.
Source: Remote Sensing
DOI: 10.3390/rs16224176
Language: English
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,069
Awards: G1113350723, G1463247041, G2601586303, Instituto de I&D em Estruturas e Construções, Instituto de I&D em Estruturas e Construções, G4046065984, G5027692329, G6714743403, Novo: Automated Visual Inspection of Railway Bridges through Computer Vision and Artificial Intelligence. Inicial: Structural Integrity Self-Assessment Applied to Railway Assets based on Physical Twin Models, G6997658380
OpenAlex ID: W4404284440
