Hanyang University researchers proposed novel technology for environmental infringement and sag estimation for transmission lines

Power transmission lines (TLs) are ubiquitous, spanning diverse landscapes, including forests, agricultural regions, as well as mountains. Therefore, it is crucial to develop novel technologies to monitor their condition in terms of environmental infringement as well as sag estimation.

In a breakthrough, a team of researchers, led by Prof. Ki-Yong Oh, Associate Professor of Mechanical Engineering at Hanyang University, and Munsu Jeon, a PhD candidate in Mechanical Convergence Engineering at Hanyang University, has presented the first unmanned aerial vehicle (UAV)-based approach that can assess both sag of TLs and environmental infringement simultaneously across the entire span. Their findings were made available online on 1 September 2025 and have been published in the journal Computer-Aided Civil and Infrastructure Engineering on 20 October 2025.

The proposed approach uses multimodal information from 3D LiDAR, GPS, and IMU to reconstruct the geometric profile of the TLs and estimate the sag of the span under actual operating conditions. Thermal imaging is then used to measure the temperature of the TLs so that the reconstructed geometry can be interpreted in relation to thermo-mechanical behavior rather than as a static shape. By linking the measured temperature with a sag–tension relationship, the present technique can also predict how the shape of the TL would change under extreme thermal conditions. This predictive capability allows the TL to be evaluated not only in its current state but also in scenarios where thermal loading increases the likelihood of environmental infringement.

Environmental infringement is evaluated by comparing the estimated sag profile with the spatial distribution of surrounding objects. The proposed approach establishes an infringement zone from the estimated sag profile with a required clearance and an uncertainty margin, and quantifies infringement by identifying environmental point cloud data within the infringement zone. Through this process, monitoring indicators such as intrusion depth, point density, and affected length are generated to support automated monitoring and prioritized vegetation management. Consequently, this technology functions not only as a geometric measurement method but also as a predictive monitoring framework that guides maintenance scheduling.

“Our technology can be used in aerial inspection and maintenance planning for overhead transmission corridors in operation. By assessing sag and environmental infringement together across the full span, this approach supports routine monitoring in areas where vegetation growth, uneven terrain, or nearby structures increase the risk of contact with transmission lines. In addition to current operating conditions, the approach can estimate how the line would deform under higher temperatures, enabling infringement to be assessed during heat waves, heavy loading, or abnormal climate events without additional on-site instrumentation,” points out Prof. Oh. 

Inspectors can use the estimated information to identify spans where the line is likely to approach nearby objects and take preventive action before interference occurs, and can also prioritize vegetation removal, address emerging clearance issues, or reinforce vulnerable sections under higher temperature or loading scenarios. The predictive insight also enables more selective planning of seasonal inspections during periods of elevated risk. In remote, forested, or obstructed segments of the corridor where field access is difficult or time-consuming, inspectors can reduce the need for climbing, on-foot surveys, or repeated site visits by relying on the aerial data. Although the team has developed this technology for transmission lines, it can also be adapted to other infrastructure where deformation and environmental interaction must be monitored together.

“As this type of monitoring becomes more common, similar principles could be applied to other infrastructure where on-site inspection is difficult or risky. This transition is also becoming more important as rapid weather shifts and unpredictable operating conditions make it difficult to rely on scheduled or in-person inspections. Automated monitoring would make it possible to detect emerging risks without waiting for visible damage or dispatching inspectors after conditions deteriorate. Instead of reacting to failures, inspections could evolve into a preventive system that identifies where intervention is needed before disruptions occur, strengthening the resilience of critical infrastructure over time,” concludes Mr. Jeon.

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About Hanyang University

Hanyang University traces its roots back to 1939 when the Dong-A Engineering Institute was established. By 1948, the institute had transformed into the nation’s first private university, evolving into Hanyang University in 1959. At its core, Hanyang University upholds the Founding Philosophy of “Love in Deed and Truth,” and its mission is to provide practical education and professional training to future experts and leaders. With a rich history spanning nearly a century, Hanyang University continues to uphold its core values while adapting to the evolving landscape of education and research, both domestically and internationally.

Website: https://www.hanyang.ac.kr/web/eng

About the authors

Prof. Ki-Yong Oh is an Associate Professor of Mechanical Engineering at Hanyang University, where he leads the Applied Dynamics & Intelligent Prognosis (ADIP) Laboratory. His research focuses on Physical AI for intelligent mobility, AI-based prognostics and health management (AI-PHM), and AI transformation (AX) technologies. His group develops approaches that connect mechanical systems with artificial intelligence to enable intelligent analysis and autonomous monitoring of complex infrastructure. These efforts aim to advance the operation and maintenance of engineering systems and strengthen the safety and resilience of future infrastructure.

Munsu Jeon is a PhD candidate in Mechanical Convergence Engineering at Hanyang University and a member of the ADIP Laboratory. His research focuses on Physical AI within intelligent mobility, combining UAVs, robotic platforms, and multimodal sensing to develop autonomous monitoring methods. He is particularly interested in integrating LiDAR, vision, and thermal data to advance mobility-based inspection and improve the safety and resilience of large-scale infrastructure.