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Hongjo Kim

Assistant Professor

Full-Time Faculty

School of Engineering: Department of Civil and Environmental Engineering and Engineering Mechanics

Contact

Email: Hongjo Kim
Phone: 937-229-3882
Website: Visit Site
Kettering Laboratories Room 465C

Bio

Dr. Hongjo Kim joined the University of Dayton as an assistant professor in 2019. He received his Ph.D. and B.S. degrees in Civil and Environmental Engineering at Yonsei University, South Korea, under the supervision of Dr. Hyoungkwan Kim. Dr. Hongjo Kim has investigated machine learning, deep learning and computer vision applications to automate construction and infrastructure management practices. While conducting his research for advanced construction and infrastructure management, he published 16 papers in top journals and registered 5 patents. His research aims at investigating new knowledge that contributes to automated data-driven decision-making from visual sensing data in civil engineering applications.

Degrees

  • Ph.D., civil and environmental engineering, Yonsei University
  • B.S., civil and environmental engineering, Yonsei University

Selected Publications

  • Kim, H., & Ham, Y. (2019). Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments. Automation in Construction, 107, 102960.
  • Jeong, H., Kim, H., & Kim, H. (2019). Optimization procedure for climate change adaptation investment planning: Case of flood disaster prevention in Seoul. Journal of Water Resources Planning and Management.
  • Bang, S., Park, S., Kim, H., & Kim, H. (2019). Encoder-decoder network for pixel-level crack detection in black-box images. Computer-Aided Civil and Infrastructure Engineering, 34, 713–727.
  • Kim, H., Ham, Y., Kim, W., Park, S., & Kim, H. (2018). Vision-based nonintrusive context documentation for earthmoving productivity simulation. Automation in Construction, 102, 135-147.
  • Park, S., Bang, S., Kim, H., & Kim, H. (2018). Patch-based crack detection in black box images using convolutional neural networks.  Journal of Computing in Civil Engineering, 33(3), 04019017.
  • Kim, H., Bang, S., Jeong, H., Ham, Y., & Kim, H. (2018). Analyzing context and productivity of tunnel earthmoving processes using imaging and simulation. Automation in Construction, 92, 188-198.
  • Ha, I., Kim, H., Park, S., & Kim, H. (2018). Image retrieval using BIM and features from pretrained VGG network for indoor localization. Building and Environment, 140, 23-31.
  • Kim, H., Kim, H., Hong, Y. W., & Byun, H. (2018). Detecting construction equipment using a region-based fully convolutional network and transfer learning. Journal of Computing in Civil Engineering, 32(2), 04017082.
  • Kim, H. & Kim, H. (2018). 3D reconstruction of construction entities for training object detectors. Automation in Construction, 88, 23-30.
  • Bang, S., Kim, H., & Kim, H. (2017). UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching. Automation in Construction, 84, 70-80.
  • Kim, K., Kim, H., & Kim, H. (2017). Image-based construction hazard avoidance system using augmented reality in wearable device. Automation in Construction, 83, 390-403.
  • Ha, S., Kim, H., Kim, K., Lee, H., & Kim, H. (2017). Algorithm for economic assessment of infrastructure adaptation to climate change. Natural Hazards Review, 16(4), 04017007.
  • Jeong, H., Kim, H., Kim, K., & Kim, H. (2017). Prediction of flexible pavement deterioration in relation to climate change using fuzzy logic. Journal of Infrastructure Systems, 23(4), 04017008.
  • Kim, H., Kim, K., & Kim, H. (2016). Data-driven scene parsing method for recognizing construction site objects in the whole image. Automation in Construction, 71(2), 271-282.
  • Kim, H., Kim, K., & Kim, H. (2016). Vision-based object-centric safety assessment using fuzzy inference: Monitoring struck-by accidents with moving objects. Journal of Computing in Civil Engineering, 30(4), 04015075.
  • Kim, B., Kim, H., & Kim, H. (2016). A framework for pricing the loss of regulating ecosystem services caused by road construction. Korean Society of Civil Engineers Journal of Civil Engineering, 20(7), 2624-2631.

Courses Taught

  • CEE 421 Construction Engineering

Professional Activities

  • Reviewer, Journal of Computing in Civil Engineering
  • Reviewer, Automation in Construction
  • Reviewer, Computer-aided Civil and Infrastructure Engineering
  • Reviewer, Canadian Journal of Civil Engineering

Research Interests

  • Infrastructure Management
  • Construction Management
  • Visual Sensing
  • Data Analytics

Work Experience

  • 2018-2019, research associate, School of Civil and Environmental Engineering, Yonsei University
  • 2018-2019, research associate, Department of Construction Science, Texas A&M University