Young‐Jin Cha
Impact in
- Civil and Structural Engineering top 0.05%
- Infrastructure Maintenance and Monitoring
- Structural Health Monitoring Techniques
- Concrete Corrosion and Durability
- Asphalt Pavement Performance Evaluation
- Geology top 0.5%
- 3D Surveying and Cultural Heritage
Papers in
-
- Structural Health Monitoring Techniques 36
- Infrastructure Maintenance and Monitoring 27
- Concrete Corrosion and Durability 13
- Vibration Control and Rheological Fluids 13
- Seismic Performance and Analysis 12
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- Non-Destructive Testing Techniques 6
- Hydraulic and Pneumatic Systems 5
- Co-authors
- Wooram Choi (6 shared papers)Oral Büyüköztürk (9 shared papers)Rahmat Ali (8 shared papers)Dong Hee Kang (1 shared paper)Anil K. Agrawal (11 shared papers)Kisung You (2 shared papers)Dong‐Ho Kang (1 shared paper)J. S. Lewis (2 shared papers)
- Journals
- Automation in Construction (8 papers)Structural Health Monitoring (7 papers)Computer-Aided Civil and Infrastructure Engineering (4 papers)Journal of Structural Engineering (3 papers)Engineering Structures (3 papers)
- Partner nations
- CanadaUnited StatesBelgium
In The Last Decade
Young‐Jin Cha
66 papers receiving 8.3k citations
Young‐Jin Cha's Hit Papers
Peers
Comparison fields: 5 of 126
- Civil and Structural Engineering 6.6k
- Geology 734
- Industrial and Manufacturing Engineering 993
- Mechanical Engineering 2.1k
- Computer Vision and Pattern Recognition 1.1k
Countries citing papers authored by Young‐Jin Cha
This map shows the geographic impact of Young‐Jin Cha's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Young‐Jin Cha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Young‐Jin Cha more than expected).
Fields of papers citing papers by Young‐Jin Cha
This network shows the impact of papers produced by Young‐Jin Cha. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Young‐Jin Cha. The network helps show where Young‐Jin Cha may publish in the future.
Co-authors
The 25 scholars most cited alongside Young‐Jin Cha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks Hit paper breakdown → | 2017 | 2492 |
| 2 | Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types Hit paper breakdown → | 2017 | 1251 |
| 3 | Modal identification of simple structures with high-speed video using motion magnification Hit paper breakdown → | 2015 | 410 |
| 4 | SDDNet: Real-Time Crack Segmentation Hit paper breakdown → | 2019 | 360 |
| 5 | Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning Hit paper breakdown → | 2020 | 318 |
| 6 | Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging Hit paper breakdown → | 2018 | 312 |
| 7 | 2016 | 261 | |
| 8 | Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage Hit paper breakdown → | 2020 | 249 |
| 9 | Efficient attention-based deep encoder and decoder for automatic crack segmentation Hit paper breakdown → | 2021 | 234 |
| 10 | 2015 | 188 | |
| 11 | Deep learning-based structural health monitoring Hit paper breakdown → | 2024 | 178 |
| 12 | Attention-based generative adversarial network with internal damage segmentation using thermography Hit paper breakdown → | 2022 | 174 |
| 13 | 2018 | 164 | |
| 14 | 2016 | 160 | |
| 15 | 2018 | 147 | |
| 16 | 2017 | 136 | |
| 17 | 2019 | 123 | |
| 18 | 2021 | 121 | |
| 19 | DNoiseNet: Deep learning-based feedback active noise control in various noisy environments Hit paper breakdown → | 2023 | 84 |
| 20 | 2023 | 77 |
About Young‐Jin Cha
Young‐Jin Cha is a scholar working on Civil and Structural Engineering, Mechanical Engineering, Ocean Engineering, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 67 papers that have together received 8.4k indexed citations. Recurring topics across this work include Structural Health Monitoring Techniques (36 papers), Infrastructure Maintenance and Monitoring (27 papers), Concrete Corrosion and Durability (13 papers), Vibration Control and Rheological Fluids (13 papers), Seismic Performance and Analysis (12 papers), Geophysical Methods and Applications (8 papers), Non-Destructive Testing Techniques (6 papers) and Hydraulic and Pneumatic Systems (5 papers). The work is most often cited by research in Civil and Structural Engineering (6.6k citations), Geology (734 citations), Industrial and Manufacturing Engineering (993 citations), Mechanical Engineering (2.1k citations) and Computer Vision and Pattern Recognition (1.1k citations). Young‐Jin Cha has collaborated with scholars based in Canada, United States and Belgium. Frequent co-authors include Wooram Choi, Oral Büyüköztürk, Rahmat Ali, Dong Hee Kang, Anil K. Agrawal, Kisung You, Dong‐Ho Kang, J. S. Lewis, Neal Wadhwa and William T. Freeman. Their work appears in journals such as Automation in Construction, Structural Health Monitoring, Computer-Aided Civil and Infrastructure Engineering, Journal of Structural Engineering and Engineering Structures.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.