Young‐Jin Cha

10.5k total citations · 11 hit papers
68 papers, 8.2k citations indexed

About

Young‐Jin Cha is a scholar working on Civil and Structural Engineering, Mechanical Engineering and Ocean Engineering. According to data from OpenAlex, Young‐Jin Cha has authored 68 papers receiving a total of 8.2k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Civil and Structural Engineering, 12 papers in Mechanical Engineering and 9 papers in Ocean Engineering. Recurrent topics in Young‐Jin Cha's work include Structural Health Monitoring Techniques (36 papers), Infrastructure Maintenance and Monitoring (27 papers) and Vibration Control and Rheological Fluids (14 papers). Young‐Jin Cha is often cited by papers focused on Structural Health Monitoring Techniques (36 papers), Infrastructure Maintenance and Monitoring (27 papers) and Vibration Control and Rheological Fluids (14 papers). Young‐Jin Cha collaborates with scholars based in Canada, United States and Belgium. Young‐Jin Cha's co-authors include Wooram Choi, Oral Büyüköztürk, Rahmat Ali, Dong‐Ho Kang, Anil K. Agrawal, Dong Hee Kang, Kisung You, Dong‐Ho Kang, Justin G. Chen and Frédo Durand and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Industrial Electronics and Construction and Building Materials.

In The Last Decade

Young‐Jin Cha

66 papers receiving 8.0k citations

Hit Papers

Deep Learning‐Based Crack Damage Detection Using Convolut... 2015 2026 2018 2022 2017 2017 2015 2019 2020 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
    2017 2.4k citations Young‐Jin Cha, Wooram Choi et al. Computer-Aided Civil and Infrastructure Engineering profile →
  2. Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types
    2017 1.2k citations Young‐Jin Cha, Wooram Choi et al. Computer-Aided Civil and Infrastructure Engineering profile →
  3. Modal identification of simple structures with high-speed video using motion magnification
    2015 399 citations Young‐Jin Cha, Oral Büyüköztürk et al. profile →
  4. SDDNet: Real-Time Crack Segmentation
    2019 343 citations Wooram Choi, Young‐Jin Cha IEEE Transactions on Industrial Electronics profile →
  5. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning
    2020 309 citations Young‐Jin Cha et al. Automation in Construction profile →
  6. Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging
    2018 304 citations Dong‐Ho Kang, Young‐Jin Cha Computer-Aided Civil and Infrastructure Engineering profile →
  7. Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage
    2020 238 citations Young‐Jin Cha et al. Structural Health Monitoring profile →
  8. Efficient attention-based deep encoder and decoder for automatic crack segmentation
    2021 221 citations Young‐Jin Cha et al. Structural Health Monitoring profile →
  9. Attention-based generative adversarial network with internal damage segmentation using thermography
    2022 163 citations Rahmat Ali, Young‐Jin Cha Automation in Construction profile →
  10. Deep learning-based structural health monitoring
    2024 138 citations Young‐Jin Cha, Rahmat Ali et al. Automation in Construction profile →
  11. DNoiseNet: Deep learning-based feedback active noise control in various noisy environments
    2023 80 citations Young‐Jin Cha et al. Engineering Applications of Artificial Intelligence profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Young‐Jin Cha Canada 31 6.5k 2.0k 1.1k 969 755 68 8.2k
Oral Büyüköztürk United States 47 7.9k 1.2× 1.6k 0.8× 986 0.9× 637 0.7× 850 1.1× 155 10.0k
Wooram Choi Canada 5 3.6k 0.6× 1.2k 0.6× 487 0.5× 709 0.7× 475 0.6× 6 4.4k
Sami F. Masri United States 56 9.0k 1.4× 2.1k 1.0× 641 0.6× 198 0.2× 411 0.5× 267 11.3k
Mohammad R. Jahanshahi United States 25 2.7k 0.4× 827 0.4× 398 0.4× 431 0.4× 292 0.4× 69 3.5k
F. Necati Çatbaş United States 42 4.8k 0.7× 706 0.3× 799 0.8× 145 0.1× 190 0.3× 169 5.5k
Billie F. Spencer United States 65 16.7k 2.6× 3.7k 1.8× 762 0.7× 245 0.3× 1.1k 1.5× 391 19.8k
Hoon Sohn South Korea 59 9.6k 1.5× 4.5k 2.2× 776 0.7× 373 0.4× 1.5k 2.0× 412 14.2k
Maria Q. Feng United States 41 5.1k 0.8× 750 0.4× 1.3k 1.2× 110 0.1× 232 0.3× 159 6.1k
James Brownjohn United Kingdom 51 7.8k 1.2× 2.1k 1.0× 777 0.7× 91 0.1× 182 0.2× 228 8.8k
Kelvin C. P. Wang United States 31 4.1k 0.6× 1.1k 0.5× 436 0.4× 291 0.3× 476 0.6× 206 4.7k

Countries citing papers authored by Young‐Jin Cha

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Young‐Jin Cha

This figure shows the co-authorship network connecting the top 25 collaborators of Young‐Jin Cha. A scholar is included among the top collaborators of Young‐Jin Cha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Young‐Jin Cha. Young‐Jin Cha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cha, Young‐Jin, et al.. (2025). Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts. Scientific Reports. 15(1). 35152–35152.
2.
Cha, Young‐Jin, et al.. (2024). 3D Pixelwise damage mapping using a deep attention based modified Nerfacto. Automation in Construction. 168. 105878–105878. 9 indexed citations
3.
Cha, Young‐Jin, Rahmat Ali, J. S. Lewis, & Oral Büyüköztürk. (2024). Deep learning-based structural health monitoring. Automation in Construction. 161. 105328–105328. 138 indexed citations breakdown →
4.
Cha, Young‐Jin, et al.. (2023). Deep learning-based active noise control on construction sites. Automation in Construction. 151. 104885–104885. 58 indexed citations
5.
6.
Kang, Dong‐Ho, et al.. (2023). Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring. Structural Health Monitoring. 23(2). 971–990. 45 indexed citations
7.
Lewis, J. S., Young‐Jin Cha, & Jongho Kim. (2023). Dual encoder–decoder-based deep polyp segmentation network for colonoscopy images. Scientific Reports. 13(1). 1183–1183. 72 indexed citations
8.
Cha, Young‐Jin, et al.. (2023). DNoiseNet: Deep learning-based feedback active noise control in various noisy environments. Engineering Applications of Artificial Intelligence. 121. 105971–105971. 80 indexed citations breakdown →
9.
Laêt, Lars De, et al.. (2020). A study of digital and physical workflows used for the creation of fabric-formed ice shells with bending active frames. International Journal of Space Structures. 36(1). 13–25. 1 indexed citations
10.
Cha, Young‐Jin, et al.. (2020). Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage. Structural Health Monitoring. 20(1). 406–425. 238 indexed citations breakdown →
11.
12.
Choi, Wooram & Young‐Jin Cha. (2019). SDDNet: Real-Time Crack Segmentation. IEEE Transactions on Industrial Electronics. 67(9). 8016–8025. 343 indexed citations breakdown →
13.
14.
Polyzois, Dimos, et al.. (2018). Deep learning-based automatic volumetric damage quantification using depth camera. Automation in Construction. 99. 114–124. 161 indexed citations
15.
Cha, Young‐Jin & Dong‐Ho Kang. (2018). Damage detection with an autonomous UAV using deep learning. 4–4. 12 indexed citations
16.
Svecova, Dagmar, et al.. (2018). Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure. Sensors. 18(4). 1018–1018. 15 indexed citations
17.
Cha, Young‐Jin, Wooram Choi, & Oral Büyüköztürk. (2017). Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks. Computer-Aided Civil and Infrastructure Engineering. 32(5). 361–378. 2429 indexed citations breakdown →
18.
Cha, Young‐Jin, Kisung You, & Wooram Choi. (2016). Vision-based detection of loosened bolts using the Hough transform and support vector machines. Automation in Construction. 71. 181–188. 252 indexed citations
19.
Cha, Young‐Jin & Anil K. Agrawal. (2012). Velocity based semi-active turbo-Lyapunov control algorithms for seismically excited nonlinear smart structures. Structural Control and Health Monitoring. 20(6). 1043–1056. 19 indexed citations
20.
Cha, Young‐Jin, Yeesock Kim, Anne Raich, & Anil K. Agrawal. (2012). Multi-objective optimization for actuator and sensor layouts of actively controlled 3D buildings. Journal of Vibration and Control. 19(6). 942–960. 27 indexed citations

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.

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