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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 has investigated data analytics, deep learning and computer vision applications to automate construction and infrastructure management practices. His research aims at investigating new knowledge that contributes to automated data-driven decision-making from visual sensing data for civil engineering applications.

Selected Publications

  • Kim, H., & Ham, Y. (2020). Increasing reliability of participatory sensing for utility pole condition assessment using fuzzy inference. Journal of Construction Engineering and Management. (Accepted; Invited Paper)
  • Jeong, H., Kim, H., & Kim, H. (2020). Optimization procedure for climate change adaptation investment planning: Case of flood disaster prevention in Seoul. Journal of Water Resources Planning and Management, 146(2), 04019077.
  • 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.
  • 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.
  • Kim, H., Kim, C., Jeong, H., Ha, S., Kim, K., & Kim, H. (2015). 4D CAD drawings based on marker-based augmented reality. Korean Journal of Construction Engineering and Management, 16(4), 30–40.

Selected Patents

  • Hyoungkwan Kim, & Hongjo Kim. (2019). Situational recognition system for construction site-based vision and method, and method for productivity analysis of earthwork using it. Patent Registration No.10-2042629. (in Korea)
  • Hyoungkwan Kim, & Hongjo Kim. (2019). 2D Image data generation system using of 3D model and method. Patent Registration No.10-1964282. (in Korea)
  • Hyoungkwan Kim, & Hongjo Kim. (2017). Estimation system and method of slope stability using 3D model and soil classification. Patent Registration No.10-1787542. (in Korea)
  • Hyoungkwan Kim, Kinam Kim, & Hongjo Kim (2017). Visualization of safety assessment system in construction site using wearable device. Patent Registration No. 10-1715001. (in Korea)
  • Hyoungkwan Kim, Hoyoung Jeong, & Hongjo Kim (2017). Generation method and system of climate change impact assessment information for infrastructure based fuzzy inference. Patent Registration No.10-1794800. (in Korea)
  • Hyoungkwan Kim, Hongjo Kim, Kinam Kim, & Hoyoung Jeong (2016). System for assessment of safety level at construction site based on computer vision. Patent Registration No.10-1674266. (in Korea)
  • Hyoungkwan Kim, Hongjo Kim, & Kinam Kim (2014). Interactive progress monitoring system using mobile terminal and user interface of the mobile terminal. Patent Application No.10-2014-0181719. (in Korea)

Selected Research and Work

  • 2020, Development of technology for securing safety of temporary structures. Agency: The Korea Agency for Infrastructure Technology and Advancement. Principle Investigator: Dr. Hongjo Kim. Award: $552,917 (663,500,000 Korean Won). Period: 2020-2025.
  • 2020, Vision-based pavement condition assessment for automated maintenance and repair decision-making. Agency: University of Dayton SEED Grant. Principle Investigator: Dr. Hongjo Kim. Award: $6,500. Period: 2020.
  • 2018, Vision-based construction and safety management: improving earthmoving productivity and disaster preparedness. Agency: Yonsei University. Principle Investigator: Dr. Hongjo Kim. Award: $33,610. Period: 2018-2019.

Selected Honors and Awards

  • 2017, Best Paper Award (2016 CEE Paper of the Year, School of Civil and Environmental Engineering, Yonsei University): Hongjo Kim, Kinam Kim, and Hyoungkwan Kim. 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.
  • 2017, Excellent Paper Award (Korean Institute of Construction Engineering and Management 2017 Conference): Hongjo Kim, Seongdeok Bang, Hoyoung Jeong, Hyoungkwan Kim (2017). Vision-based monitoring and construction process simulation for productivity analysis of an earthmoving process in a tunnel. 2017 Korea Institute of Construction Engineering and Management Conference, 47-48.
  • 2016, Excellent Paper Award (2016 Korean Society of Civil Engineers Conference): Hongjo Kim, Kinam Kim, Seongdeok Bang, Hyoungkwan Kim (2016). 3D reconstruction of construction entities from images using multi-view stereo. 2016 Korean Society of Civil Engineers Conference, 25-26.
  • 2014, Excellent Paper Award (2014 Korean Institute of Construction Engineering and Management Conference): Hongjo Kim, Hoyoung Jeong, Kinam Kim, Sungjae Park, Changyoon Kim, Hyoungkwan Kim (2014).  A fuzzy inference- and computer vision-based safety assessment system for construction site entities. 2014 Korea Institute of Construction Engineering and Management Conference, 99-100.
  • 2013, Excellent Paper Award (2013 Korean Institute of Construction Engineering and Management Conference): Changyoon Kim, Hongjo Kim, Sungmo Ahn, Hyoungkwan Kim. BIM-based construction site layout planning for caisson structure fabrication using process simulation. 2013 Korea Institute of Construction Engineering and Management Conference, 193-194.
  • 2012, Silver Prize, Programming Competition, College of Engineering, Yonsei University.

Degrees

  • Ph.D., School of Civil and Environment Engineering, Yonsei University
  • B.S., School of Civil and Environment Engineering, Yonsei University

Courses Taught

  • CEE 421 Construction Engineering
  • CEE 595 08 Introduction to Data Analytics for Engineering Systems

Professional Activities

  • Member, ASCE Construction Research Council (CRC), 2020-present
  • Member, American Society of Civil Engineers (ASCE), 2019-present
  • Member, Korea Institute of Construction Engineering and Management (KICEM), 2013-present

Research Interests

  • Construction and Infrastructure Management
  • Computer Vision
  • 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