aalto1 untyped-item.component.html
Drone-based bridge health monitoring and inspection
Loading...
URL
Journal Title
Journal ISSN
Volume Title
Insinööritieteiden korkeakoulu |
Bachelor's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Authors
Date
Department
Major/Subject
Mcode
ENG3082
Degree programme
Language
en
Pages
26 + 6
Series
Abstract
Unmanned aerial vehicles (UAVs) are displacing bucket trucks and rope access in bridge inspections by delivering safer, data-rich, repeatable workflows. This thesis is a state-of-the-art review that benchmarks multirotor, fixed-wing, and hybrid VTOL airframes, finding multirotors optimal for close imaging and hybrids for kilometer-scale corridors. It reviews payloads, RGB, infrared thermography, LiDAR, ultrasonic echo, and ground-penetrating radar, and shows photogrammetry now yields sub-millimeter orthomosaics, while LiDAR point clouds provide centimetric geometry for deformation analysis. Deep-learning detectors (YOLO variants) reach 98 % crack-detection precision; lightweight segmentation networks enable onboard, real-time feedback. Field trials halve site time, eliminate lane closures, and lower inspector risk, yet 20-30 min battery endurance and GNSS drop-outs beneath decks still constrain operations. The study concludes that fusing multi-sensor UAV data with Bridge Information Models and Bridge Management Systems yields objective condition ratings; wider adoption hinges on open data schemas, longer-endurance airframes, and resilient vision-based navigation.