World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
60
Citations
11863
World Ranking
2223
National Ranking
447

Lieyun Ding 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 Lieyun Ding 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: 158 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Lieyun Ding 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 Lieyun Ding 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: 60 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Overview

Lieyun Ding is affiliated with Huazhong University of Science and Technology in China, focusing primarily on engineering research. Their work is particularly concentrated in several key scientific fields and subfields including civil and structural engineering, building and construction, radiological and ultrasound technology, astronomy and astrophysics, and aerospace engineering.

The main topics covered in their research encompass occupational health and safety research, infrastructure maintenance and monitoring, planetary science and exploration, building information modeling (BIM) and construction integration, risk and safety analysis, blockchain technology applications and security, and geotechnical engineering and analysis.

Lieyun Ding has contributed to numerous publications in a variety of scientific venues, with frequent contributions to:

  • Automation in Construction
  • Engineering
  • Developments in the Built Environment
  • Frontiers of Engineering Management
  • Advanced Engineering Informatics

The researcher has coauthored multiple papers with several frequent collaborators, including:

  • Cheng Zhou
  • Ying Zhou
  • Chenshuang Li
  • Peter E.D. Love
  • Yuyue Gao

Some recent significant papers authored by Lieyun Ding include:

  • "Machine learning in construction: From shallow to deep learning," 2021, Developments in the Built Environment
  • "Construction quality information management with blockchains," 2020, Automation in Construction
  • "Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology," 2020, Automation in Construction
  • "Hyperledger fabric-based consortium blockchain for construction quality information management," 2020, Frontiers of Engineering Management
  • "Deep learning and network analysis: Classifying and visualizing accident narratives in construction," 2020, Automation in Construction

Best Publications

  • Detecting non-hardhat-use by a deep learning method from far-field surveillance videos

    Qi Fang;Qi Fang;Heng Li;Xiaochun Luo;Lieyun Ding

  • A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

    Lieyun Ding;Weili Fang;Hanbin Luo;Peter E.D. Love

  • Building Information Modeling (BIM) application framework: The process of expanding from 3D to computable nD

    Lieyun Ding;Ying Zhou;Ying Zhou;Burcu Akinci

  • Falls from heights: A computer vision-based approach for safety harness detection

    Weili Fang;Lieyun Ding;Hanbin Luo;Peter E.D. Love

  • Non-linear description of ground settlement over twin tunnels in soil

    Ling Ma;Lieyun Ding;Hanbin Luo

  • Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach

    Weili Fang;Lieyun Ding;Botao Zhong;Peter E.D. Love

  • Computer vision for behaviour-based safety in construction: A review and future directions

    Weili Fang;Peter E.D. Love;Hanbin Luo;Lieyun Ding

  • Machine learning in construction: From shallow to deep learning

    Yayin Xu;Ying Zhou;Przemyslaw Sekula;Przemyslaw Sekula;Lieyun Ding

  • Construction risk knowledge management in BIM using ontology and semantic web technology

    L.Y. Ding;B.T. Zhong;Song Wu;H.B. Luo

  • Improved Fuzzy Bayesian Network-Based Risk Analysis With Interval-Valued Fuzzy Sets and D–S Evidence Theory

    Yue Pan;Limao Zhang;ZhiWu Li;Lieyun Ding

  • Computer vision applications in construction safety assurance

    Weili Fang;Lieyun Ding;Peter E.D. Love;Hanbin Luo

  • Real-time safety early warning system for cross passage construction in Yangtze Riverbed Metro Tunnel based on the internet of things

    L.Y. Ding;C. Zhou;Q.X. Deng;H.B. Luo

  • Construction quality information management with blockchains

    Da Sheng;Lieyun Ding;Botao Zhong;Peter E.D. Love

  • Ontology-based semantic modeling of regulation constraint for automated construction quality compliance checking

    B.T. Zhong;L.Y. Ding;H.B. Luo;Y. Zhou

  • Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology

    Weili Fang;Ling Ma;Peter E.D. Love;Hanbin Luo

  • Digital reproduction of historical building ornamental components: From 3D scanning to 3D printing

    Jie Xu;Lieyun Ding;Peter E.D. Love

  • Safety barrier warning system for underground construction sites using Internet-of-Things technologies

    C. Zhou;L.Y. Ding

  • Application of 4D visualization technology for safety management in metro construction

    Y. Zhou;L.Y. Ding;L.J. Chen

  • Dynamic prediction for attitude and position in shield tunneling: A deep learning method

    Cheng Zhou;Hengcheng Xu;Lieyun Ding;Linchun Wei

  • Computer vision aided inspection on falling prevention measures for steeplejacks in an aerial environment

    Qi Fang;Qi Fang;Heng Li;Xiaochun Luo;Lieyun Ding

  • Hyperledger fabric-based consortium blockchain for construction quality information management

    Botao Zhong;Haitao Wu;Lieyun Ding;Hanbin Luo

  • A deep learning-based method for detecting non-certified work on construction sites

    Qi Fang;Qi Fang;Heng Li;Xiaochun Luo;Lieyun Ding

  • Development of web-based system for safety risk early warning in urban metro construction

    L.Y. Ding;C. Zhou

Frequent Co-Authors

Hanbin Luo
Hanbin Luo Huazhong University of Science and Technology
Miroslaw J. Skibniewski
Miroslaw J. Skibniewski University of Maryland, College Park
Peter E.D. Love
Peter E.D. Love Curtin University
Heng Li
Heng Li Hong Kong Polytechnic University
Limao Zhang
Limao Zhang Huazhong University of Science and Technology
Xiangyu Wang
Xiangyu Wang Curtin University
Yi-Qing Ni
Yi-Qing Ni Hong Kong Polytechnic University
Xiaoling Zhang
Xiaoling Zhang University of Hong Kong
Jim Smith
Jim Smith Bond University
Feniosky Peña-Mora
Feniosky Peña-Mora Columbia University

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