Tengjiao Jiang
About
Postdoc project: Computer vision‐based structural monitoring (COSMO)
Intelligent and automated inspection methods are receiving more and more attention with the long-term operation of many transportation infrastructures. Except for the continuous degradation of the material performance, human and natural loads (e.g. repeated loads from vehicles, wind and earthquakes) will result in cumulative or sudden damage to infrastructures, threatening safe operation and possibly leading to disasters.
Dr. Tengjiao Jiang focuses on developing inspection and evaluation methods for existing railways and bridges by using computer vision (CV) and deep learning (DL). The project is funded by the Norwegian Railway Directorate.
Research interests
- Computer Vision (CV)
- Machine Learning (ML)
- Photogrammetry
- Structural Health Monitoring (SHM)
- Structural Dynamics and Signal Processing
Academic Activities
- Associate Editor of the Journal Experimental Techniques
- Chair of Session "Vision-based Monitoring of Civil Structures" at the IMAC-XLI, Austin, Texas, USA, Feb. 13–16, 2023
Research Highlights
- A robust line-tracking photogrammetry method for uplift measurements of railway catenary systems in noisy backgrounds [PDF]
- A novel line-tracking technique from noisy backgrounds [Open-source code]
Supervisors
Media
2020
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Academic lecture(2020) Application of a Robust Photogrammetry Method in Uplift Measurements of Railway Catenary Systems in Noisy Backgrounds. IMAC XXXVIII 2020 . Society for Experimental Mechanics, Inc.; Houston, Texas. 2020-02-10 - 2020-02-13.