Prof. Youngjin Cha, Ph.D., P.Eng. 

Laboratory for Infrastructure Science and Technology (LIST)

Prof. Cha’s essential interest includes the development of advanced deep learning methods for smart sustainable structural systems using advanced structural health monitoring systems and control technologies.

Dr. Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba. He received his Ph.D. in structural engineering from Texas A&M University, College Station, Texas in 2008, and served as a Postdoctoral Associate at the Massachusetts Institute of Technology (M.I.T). His key scientific contribution is deep learning-based automated SHM with autonomous unmanned aerial vehicles (UAVs). He brought this topic to light with paper publications in top-ranking journals. Researchers, professors, scientists, students, and industry professionals from many countries have shown a strong interest in this innovative topic since 2016 and have conducted numerous follow-up studies. His journal article (Cha et al., 2017) published in CACAIE which is a top 1-2 ranked journal in the entire civil engineering field was reported as the lifetime topmost highly cited paper, and he was also reported as the lifetime topmost highly cited author in this same journal. In 2024, Elsevier Data Repository identified him as one of the most cited scientists in the field of acoustics (structural health monitoring), ranking in the top 0.12%, and in the entire science and engineering fields, where he ranked in the top 0.24% for single-year impact worldwide without self-citations. He has been reported several hundred times as the most read author, with the most read research items and citations in the Civil Engineering Department at U of M, with more than 120,000 readings since his account on www.researchgate.net was opened (retrieved on October, 2024).

 

Research Interests:

His main scientific research interests are categorized as self-monitoring, healing, and controlling multi-functional sustainable structural systems. Consequently, the following are possible areas of research:

  • Deep Learning-based structural health monitoring (SHM) for sustainable civil structures
  • Deep Learning-based engineering problem solving
  • Deep Learning-based smart structure design and control
  • Autonomous navigation of unmanned aerial vehicles for SHM
  • Automation of civil engineering problems.
  • Nonlinear system identification based on Bayesian recursive estimation
  • Unsupervised approaches for damage detection using deep learning.
  • Optimal sensor distribution of wireless sensors for SHM
  • Structural modal updating based damage detection
  • Passive, active, semi-active, and hybrid control for sustainable high-rise buildings and bridges subjected to multi-hazardous loads to improve resiliency and reliability
  • Effective performance-based design for multi-hazards (i.e., wind, seismic, blast, and impact) of high-rise buildings and bridges
  • Large-scale real-time hybrid testing of civil structures for natural or man-made hazards
  • Structural dynamics and nonlinear model and seismic design and analysis
  • Self-monitoring, self-healing, and self-controlling structural units for sustainable infrastructures

Contact information:

Dr. Youngjin Cha, Ph.D., P.Eng.

Professor
Department of Civil Engineering
University of Manitoba
15 Gillson Street
Winnipeg, MB R3T 5V6
Office: (204) 272-1646
Email: young.cha@umanitoba.ca

Academic Editor, Structural Control & Health Monitoring, Wiley
Associate Editor, Structural Health Monitoring, SAGE
Associate Editor, Engineering Reports, Wiley

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