Dr. Youngjin Cha

Area

• Fully automated deep learning–based automated urban system and infrastructure health monitoring systems that integrate deep learning and autonomous unmanned aerial vehicles (UAVs),
• Digitization of urban infrastructure through computer vision, robotics and autonomous UAVs, and unmanned ground vehicles (UGVs)
• Smart transportation system with automated monitoring and inspection
• Holistic 3-D damage mapping with autonomous UAVs and UGVs
• 3-D reconstruction, digital twin
• Non-contact remote sensing using video cameras, laser scanners, and LIDAR
• Earthquake control using an advanced hybrid control system
• Computational modelling and simulation using FEM for high nonlinear analysis
• Nonlinear hysteretic behaviour prediction through advanced deep learning algorithms
• Deep learning–based detection of diseases within medical images and defects in plant and industrial products

Expertise

Deep learning, structural dynamics, structural control, structural health monitoring, autonomous UAVs, automation, digital twin.

Research description

Dr. Cha has made significant contributions to his field, securing $1.757 million in research funding as a Principal Investigator (PI) and submitting an additional $1.7 million funding proposal as a PI. His work has resulted in over 100 publications in peer-reviewed journals and internationally recognized conferences, along with six issued patents with the U.S. Patent Office. He has also delivered more than 100 presentations at international webinars, forums, seminars, and conferences, including an award lecture and a keynote address.

Dr. Cha has made unique strides in his field, primarily through pioneering the use of deep learning for monitoring the health of built environments. This concept, which he first introduced in paper publications in CACIE (Wiley) (Cha et al., 2017; Cha et al., 2018), employs deep learning-based damage identification methods. These methods have effectively addressed over three decades of enduring engineering challenges in data and signal analysis by automatically extracting signal features using deep learning algorithms. This signifies a paradigm shift in addressing a wide range of engineering problems, providing a foundational solution for various engineering challenges, including but not limited to infrastructure monitoring. This research has led to a substantial increase in his publication citations, with Cha et al., 2017 and 2018, being recognized as the lifetime most and second-most cited articles and in 2022 and 2023, respectively. Since 2024, he has been acknowledged as the lifetime topmost highly cited author in the history of CACIE, a journal that holds the top rank 2.

Another significant breakthrough from his research involves the development of the first autonomous flight method for UAVs in GPS-denied areas, specifically for infrastructure monitoring (Kang and Cha, 2018). The autonomous UAV was paired with his deep learning method (Cha et al., 2017) for infrastructure monitoring. Moreover, an advanced autonomous flight method, which uses sophisticated deep learning techniques, was integrated and utilized to identify large-scale infrastructure damage (Ali et al., 2021; Waqas et al., 2023) through deep learning based digital twins and 3D damage mapping (Kim and Cha, 2024). Currently, there is ongoing research into automation technologies, monitoring, inspections, robotics and IoT applications, and digital twin technologies through advanced AI for built environments and automated construction.

According to Google Scholar Citations, he has accrued a total of 13,446 citations (March, 2026), with 2,532 received in 2025 alone. He is considered the top scientist in his field of research that is deep learning based structural health monitoring (SHM) in North America. These citation statistics place him in the top 0.26%, 0.29%, 0.31%, 0.31%, 0.45%, and 0.65% globally in the fields of civil engineering for 2024, 2023, 2022, 2021, 2020, and 2019, respectively. These rankings, based on assessments conducted by Elsevier and Stanford University using c-score metrics or the top 2% percentile in their respective sub-fields. Clarivate (Web of Science) recognized him as a “Highly Cited Researcher (HCR)” in 2025, placing him globally within the top 0.1% of the Engineering field.
 

Professor Youngjin Cha received his PhD (2008) from Texas A&M University in the Department of Civil and Environmental Engineering, his M.Sc. (2004) from Yonsei University, and his B.Eng. (2002) from Kumoh National Institute of Technology. He started as a post-doctoral fellow (2009) at the City College of New York and then became a post-doctoral associate at the Massachusetts Institute of Technology (MIT) (2012). He joined the Department of Civil Engineering at the University of Manitoba (U of M) in 2014.

In 2024, he was elected a Fellow of ASCE for his significant contributions to the society, particularly through innovative methods in structural health monitoring (SHM). He received the Merit Award in the Research, Scholarly Work, and Creative Activities category in 2021 from the joint committee of the University of Manitoba (UM) and the UM Faculty Association. In 2022, he was honored with the IAAM Scientist Award in “Smart Materials and Structures” at the Advanced Materials World Congress, organized by the International Association of Advanced Materials (IAAM). In 2023, he received the UM Graduate Students’ Association (UMGSA) Teaching Award—an honor granted annually to only one faculty member across the entire University.

In addition to these awards, Dr. Cha has been invited to serve in several distinguished editorial roles, including Associate Editor for IEEE Transactions on Industrial Informatics (IF: 9.9), Engineering Applications of Artificial Intelligence (IF: 8.0, Elsevier), Structural Health Monitoring (IF: 5.7, SAGE), and Engineering Reports (IF: 2.0, Wiley). He also serves as Academic Editor for Structural Control & Health Monitoring (Wiley), and Journal of Advanced Transportation (IF: 1.8, Wiley). Previously, he served as an Associate Editor for the International Conference on Pattern Recognition (IAPR) in 2022, the flagship conference of the International Association of Pattern Recognition.

Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361-378.

Cha, Y. J., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018). Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types. Computer‐Aided Civil and Infrastructure Engineering, 33(9), 731-747.

Chen, J. G., Wadhwa, N., Cha, Y. J., Durand, F., Freeman, W. T., & Buyukozturk, O. (2015). Modal identification of simple structures with high-speed video using motion magnification. Journal of Sound and Vibration, 345, 58-71.

Cha, Y. J., & Buyukozturk, O. (2015). Structural damage detection using modal strain energy and hybrid multiobjective optimization. Computer‐Aided Civil and Infrastructure Engineering, 30(5), 347-358.

Kang, D., & Cha, Y. J. (2018). Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo‐tagging. Computer‐Aided Civil and Infrastructure Engineering, 33(10), 885-902.

Beckman, G. H., Polyzois, D., & Cha, Y. J. (2019). Deep learning-based automatic volumetric damage quantification using depth camera. Automation in Construction, 99, 114-124.

Choi, W., & Cha, Y. J. (2019). SDDNet: Real-time crack segmentation. IEEE Transactions on Industrial Electronics, 67(9), 8016-8025.

Kang, D., Benipal, S. S., Gopal, D. L., & Cha, Y. J. (2020). Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Automation in Construction, 118, 103291.

Kang, D. H., & Cha, Y. J. (2022). Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring, 21(5), 2190-2205.

Ali, R., & Cha, Y. J. (2022). Attention-based generative adversarial network with internal damage segmentation using thermography. Automation in Construction, 141, 104412.

Ali, R., Kang, D., Suh, G., & Cha, Y. J. (2021). Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures. Automation in Construction, 130, 103831.

Wang, Z., & Cha, Y. J. (2021). 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.

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