World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
35
Citations
5522
World Ranking
8952
National Ranking
241

Jeong-Tae Kim publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Jeong-Tae Kim sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 223 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Jeong-Tae Kim D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Jeong-Tae Kim sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 35 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Jeong-Tae Kim is affiliated with Pukyong National University in South Korea and focuses research primarily within the field of Engineering. Their work is concentrated in several subfields including Civil and Structural Engineering, Mechanics of Materials, Biomedical Engineering, Mechanical Engineering, and Computer Vision and Pattern Recognition.

The main topics addressed in Kim's research cover a range of areas related to structural assessment and maintenance. These include Structural Health Monitoring Techniques, Ultrasonics and Acoustic Wave Propagation, Infrastructure Maintenance and Monitoring, Non-Destructive Testing Techniques, Concrete Corrosion and Durability, Smart Materials for Construction, and Structural Engineering and Vibration Analysis.

A selection of recent papers authored or coauthored by Jeong-Tae Kim provides insight into the scope of their work:

  • Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model (2020), published in Sensors
  • A method for automated bolt-loosening monitoring and assessment using impedance technique and deep learning (2023), published in Developments in the Built Environment
  • Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms (2020), published in Sensors
  • Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data (2022), published in Sensors
  • Monolithic 3D micromixer with an impeller for glass microfluidic systems (2020), published in Lab on a Chip

These papers indicate a strong focus on the use of deep learning and advanced imaging techniques to monitor and assess structural components such as bolts in engineering infrastructure.

Frequent coauthors collaborating with Kim include:

  • Quoc-Bao Ta
  • Thanh-Canh Huynh
  • Quang-Quang Pham
  • Ngoc-Lan Pham
  • Ngoc-Loi Dang

Regarding publication venues, Kim has contributed extensively to Sensors, with 12 publications there. Other venues with multiple contributions include Structural Control and Health Monitoring, Journal of Civil Structural Health Monitoring, Structural Health Monitoring, and Journal of Fisheries and Marine Sciences Education.

Best Publications

  • Damage identification in beam-type structures: frequency-based method vs mode-shape-based method

    Jeong-Tae Kim;Yeon-Sun Ryu;Hyun-Man Cho;Norris Stubbs

  • Damage Localization in Structures Without Baseline Modal Parameters

    Norris Stubbs;Jeong-Tae Kim

  • Crack detection in beam-type structures using frequency data

    J.-T. Kim;N. Stubbs

  • Field Verification of a Nondestructive Damage Localization and Severity Estimation Algorithm

    N. Stubbs;J. T. Kim;C. R. Farrar

  • Improved damage identification method based on modal information

    J.-T. Kim;N. Stubbs

  • Model-Uncertainty Impact and Damage-Detection Accuracy in Plate Girder

    Jeong-Tae Kim;Norris Stubbs

  • Vibration-based damage monitoring in model plate-girder bridges under uncertain temperature conditions

    Jeong-Tae Kim;Jae-Hyung Park;Byung-Jun Lee

  • An investigation into unified chassis control scheme for optimised vehicle stability and manoeuvrability

    Wanki Cho;Jangyeol Yoon;Jeongtae Kim;Jaewoong Hur

  • Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing

    Thanh-Canh Huynh;Jae-Hyung Park;Hyung-Jo Jung;Jeong-Tae Kim

  • Hybrid health monitoring of prestressed concrete girder bridges by sequential vibration-impedance approaches

    Jeong-Tae Kim;Jae-Hyung Park;Dong-Soo Hong;Woo-Sun Park

  • Identification of prestress-loss in PSC beams using modal information

    Jeong-Tae Kim;Chung-Bang Yun;Yeon-Sun Ryu;Hyun-Man Cho

  • Vision-based technique for bolt-loosening detection in wind turbine tower

    Jae-Hyung Park;Thanh-Canh Huynh;Sang-Hoon Choi;Jeong-Tae Kim

  • Damage Detection In Offshore Jacket Structures From Limited Modal Information

    Jeong-Tae Kim;Norris Stubbs

  • Autonomous smart sensor nodes for global and local damage detection of prestressed concrete bridges based on accelerations and impedance measurements

    Jae Hyung Park;Jeong Tae Kim;Dong Soo Hong;David Mascarenas

  • Preload Monitoring in Bolted Connection Using Piezoelectric-Based Smart Interface

    Thanh-Canh Huynh;Ngoc-Loi Dang;Jeong-Tae Kim

  • Vibration and impedance monitoring for prestress-loss prediction in PSC girder bridges

    Jeong-Tae Kim;Jae-Hyung Park;Dong-Soo Hong;Hyun-Man Cho

  • Sequential damage detection approaches for beams using time-modal features and artificial neural networks

    Jae-Hyung Park;Jeong-Tae Kim;Dong-Soo Hong;Duc-Duy Ho

  • Impedance-Based Cable Force Monitoring in Tendon-Anchorage Using Portable PZT-Interface Technique

    Thanh-Canh Huynh;Jeong-Tae Kim

  • Nondestructive Crack Detection Algorithm for Full-Scale Bridges

    Jeong-Tae Kim;Norris Stubbs

  • Quantification of temperature effect on impedance monitoring via PZT interface for prestressed tendon anchorage

    Thanh-Canh Huynh;Jeong-Tae Kim

  • A method for automated bolt-loosening monitoring and assessment using impedance technique and deep learning

    Unknown

  • Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model.

    Hai Chien Pham;Quoc-Bao Ta;Jeong-Tae Kim;Duc-Duy Ho

  • RBFN‐based temperature compensation method for impedance monitoring in prestressed tendon anchorage

    Thanh‐Canh Huynh;Jeong‐Tae Kim

  • Structural identification of cable-stayed bridge under back-to-back typhoons by wireless vibration monitoring

    Thanh-Canh Huynh;Jae-Hyung Park;Jeong-Tae Kim

  • Smart PZT-interface for wireless impedance-based prestress-loss monitoring in tendon-anchorage connection

    Khac Duy Nguyen;Jeong-Tae Kim

Frequent Co-Authors

Chung Bang Yun
Chung Bang Yun Korea Advanced Institute of Science and Technology
Masanobu Shinozuka
Masanobu Shinozuka Columbia University
Hyung-Jo Jung
Hyung-Jo Jung Korea Advanced Institute of Science and Technology
Hong-Nan Li
Hong-Nan Li Dalian University of Technology
Gangbing Song
Gangbing Song University of Houston
Jerome P. Lynch
Jerome P. Lynch University of Michigan–Ann Arbor
Maria Q. Feng
Maria Q. Feng Columbia University
Charles R. Farrar
Charles R. Farrar Los Alamos National Laboratory
Yoon-Koo Kang
Yoon-Koo Kang Asan Medical Center
Ting-Hua Yi
Ting-Hua Yi Dalian University of Technology

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