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Hyoungkwan Kim

Hyoungkwan Kim

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
6540
World Ranking
6612
National Ranking
175

Overview

Hyoungkwan Kim is a researcher affiliated with Yonsei University in South Korea, with a focus on engineering and earth and planetary sciences. Their academic work spans several interdisciplinary areas, including civil and structural engineering, geology, computer vision and pattern recognition, building and construction, and artificial intelligence.

The primary research topics covered by Kim include infrastructure maintenance and monitoring, 3D surveying and cultural heritage, building information modeling (BIM) and construction integration, construction engineering and safety, advanced neural network applications, robotics and sensor-based localization, and remote sensing and LiDAR applications.

Kim's recent published papers cover topics involving the intersection of artificial intelligence and construction automation. Notable publications include:

  • Image augmentation to improve construction resource detection using generative adversarial networks, cut-and-paste, and image transformation techniques (2020, Automation in Construction)
  • Deep learning-based 3D reconstruction of scaffolds using a robot dog (2021, Automation in Construction)
  • Synthetic data generation using building information models (2021, Automation in Construction)
  • Question answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers (2021, Automation in Construction)
  • Context-based information generation for managing UAV-acquired data using image captioning (2020, Automation in Construction)

The frequent collaborators in Kim's research include Juhyeon Kim, Jeehoon Kim, Duho Chung, Seongdeok Bang, and Somin Park. These co-authorships suggest a collaborative approach to research within the fields of construction automation and data-driven infrastructure analysis.

Kim's scholarly articles appear primarily in venues focused on civil engineering and construction technology. The most frequent publication outlets are:

  • Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC)
  • Automation in Construction
  • Journal of Computing in Civil Engineering
  • Computer-Aided Civil and Infrastructure Engineering
  • Journal of Management in Engineering

The research spans multiple subfields reflecting an integration of traditional engineering disciplines with emerging technologies such as artificial intelligence and robotics, particularly applied to construction and infrastructure monitoring. This includes development and application of deep learning techniques for 3D reconstruction and synthetic data generation, as well as the use of transformer-based models for damage information retrieval.

Best Publications

  • Computer vision techniques for construction safety and health monitoring

    JoonOh Seo;SangUk Han;SangHyun Lee;Hyoungkwan Kim

  • Encoder–decoder network for pixel-level road crack detection in black-box images

    Seongdeok Bang;Somin Park;Hongjo Kim;Hyoungkwan Kim

  • Detecting Construction Equipment Using a Region-Based Fully Convolutional Network and Transfer Learning

    Hongjo Kim;Hyoungkwan Kim;Yong Won Hong;Hyeran Byun

  • Real options analysis for renewable energy investment decisions in developing countries

    Kyeongseok Kim;Hyoungbae Park;Hyoungkwan Kim

  • Image-based construction hazard avoidance system using augmented reality in wearable device

    Kinam Kim;Hongjo Kim;Hyoungkwan Kim

  • On-site construction management using mobile computing technology

    Changyoon Kim;Taeil Park;Hyunsu Lim;Hyoungkwan Kim

  • Integrating 3D visualization and simulation for tower crane operations on construction sites

    Mohamed Al-Hussein;Muhammad Athar Niaz;Haitao Yu;Hyoungkwan Kim

  • Vision-Based Object-Centric Safety Assessment Using Fuzzy Inference: Monitoring Struck-By Accidents with Moving Objects

    Hongjo Kim;Kinam Kim;Hyoungkwan Kim

  • Structuring the prediction model of project performance for international construction projects: A comparative analysis

    Du Y. Kim;Seung H. Han;Hyoungkwan Kim;Heedae Park

  • Using hue, saturation, and value color space for hydraulic excavator idle time analysis

    Junhao Zou;Junhao Zou;Hyoungkwan Kim;Hyoungkwan Kim

  • Analyzing Schedule Delay of Mega Project: Lessons Learned From Korea Train Express

    Seung Heon Han;Sungmin Yun;Hyoungkwan Kim;Young Hoon Kwak

  • 4D CAD model updating using image processing-based construction progress monitoring

    Changyoon Kim;Byoungil Kim;Hyoungkwan Kim

  • Augmented reality system for facility management using image-based indoor localization

    Francis Baek;Inhae Ha;Hyoungkwan Kim

  • Image retrieval using BIM and features from pretrained VGG network for indoor localization

    Inhae Ha;Hongjo Kim;Somin Park;Hyoungkwan Kim

  • Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks

    Somin Park;Seongdeok Bang;Hongjo Kim;Hyoungkwan Kim

  • Predicting Profit Performance for Selecting Candidate International Construction Projects

    Seung H. Han;Du Y. Kim;Hyoungkwan Kim

  • UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching

    Seongdeok Bang;Hongjo Kim;Hyoungkwan Kim

  • Image augmentation to improve construction resource detection using generative adversarial networks, cut-and-paste, and image transformation techniques

    Seongdeok Bang;Francis Baek;Somin Park;Wontae Kim

  • Analyzing context and productivity of tunnel earthmoving processes using imaging and simulation

    Hongjo Kim;Hongjo Kim;Seongdeok Bang;Hoyoung Jeong;Youngjib Ham

  • Deep learning-based 3D reconstruction of scaffolds using a robot dog

    Unknown

  • Causes of Bad Profit in Overseas Construction Projects

    Seung H. Han;Sang H. Park;Du Y. Kim;Hyoungkwan Kim

  • Greenhouse Gas Emissions from Onsite Equipment Usage in Road Construction

    Byungil Kim;Hyounkyu Lee;Hyungbae Park;Hyoungkwan Kim

Frequent Co-Authors

Carl T. Haas
Carl T. Haas University of Waterloo
SangHyun Lee
SangHyun Lee University of Michigan–Ann Arbor
Mohamed Al-Hussein
Mohamed Al-Hussein University of Alberta
Simaan AbouRizk
Simaan AbouRizk University of Alberta
Osmar R. Zaïane
Osmar R. Zaïane University of Alberta
Taehoon Hong
Taehoon Hong Yonsei University
Bin Han
Bin Han University of Alberta
Young Hoon Kwak
Young Hoon Kwak George Washington University

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