Jinglun Feng
Papers
15
Total Citations
190
H-Index
7
About
Jinglun Feng is a robotics and infrastructure inspection researcher whose work sits at the intersection of autonomous systems, non-destructive evaluation (NDE), and applied machine learning. His research focuses primarily on developing robotic platforms and intelligent algorithms to automate the inspection of civil infrastructure — both above and below ground — reducing reliance on manual, time-intensive processes. Feng's most impactful contribution is his development of a wall-climbing robot for concrete construction inspection (2022, 59 citations), a system that enables metric-level assessment of built structures to ensure compliance with building codes and support aging infrastructure management. Complementing this, he has pioneered robotic ground penetrating radar (GPR) systems for underground utility mapping, advancing both automated data collection and deep learning-based 3D subsurface reconstruction through frameworks like GPRNet (2021, 18 citations), earning over 25 citations per related study. His multi-modal inspection work — fusing GPR, impact-echo, and visual sensing — reflects a broader vision of comprehensive structural health monitoring. More recently, Feng has explored neural radiance fields for autonomous robotics (2024). Collectively, his publications have accumulated nearly 180 citations, establishing him as an emerging leader in intelligent robotic inspection for construction and infrastructure applications.
Research Focus
Key Achievements
Top Papers
- 1Automated wall‐climbing robot for concrete construction inspection59 citations · 2022
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- 4GPR-based Model Reconstruction System for Underground Utilities Using GPRNet18 citations · 2021
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- 6Benchmarking neural radiance fields for autonomous robots: An overview13 citations · 2024
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