World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
41
Citations
5570
World Ranking
7080
National Ranking
1301

Yuequan Bao 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 Yuequan Bao 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: 93 publications — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Yuequan Bao 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 Yuequan Bao 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: 41 D-Index — 31st percentile

31% of scientists in this discipline score the same or lower.

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

Overview

Yuequan Bao is affiliated with the Harbin Institute of Technology in China, contributing extensively to the field of engineering with a particular focus on civil and structural engineering. Their research portfolio encompasses an array of publications primarily centered on structural health monitoring and related technical domains.

Their recent notable papers include:

  • Machine learning paradigm for structural health monitoring, 2020, Structural Health Monitoring
  • An active learning method combining deep neural network and weighted sampling for structural reliability analysis, 2020, Mechanical Systems and Signal Processing
  • Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring, 2020, Structural Health Monitoring
  • Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer, 2020, Structural Health Monitoring
  • Transfer learning-based data anomaly detection for structural health monitoring, 2023, Structural Health Monitoring

Frequent coauthors collaborating with Yuequan Bao include:

  • Hui Li
  • Yang Xu
  • Zhiyi Tang
  • Huabin Sun
  • Xiaoshu Guan

The main publication venues where Yuequan Bao often contributes are:

  • Structural Health Monitoring
  • Reliability Engineering & System Safety
  • Structural Control and Health Monitoring
  • Mechanical Systems and Signal Processing
  • Engineering Structures

Their primary field of study is engineering, with a strong emphasis on subfields including:

  • Civil and Structural Engineering
  • Statistics, Probability and Uncertainty
  • Mechanical Engineering
  • Mechanics of Materials
  • Artificial Intelligence

Research topics that Yuequan Bao has extensively worked on cover:

  • Structural Health Monitoring Techniques
  • Infrastructure Maintenance and Monitoring
  • Concrete Corrosion and Durability
  • Probabilistic and Robust Engineering Design
  • Ultrasonics and Acoustic Wave Propagation
  • Non-Destructive Testing Techniques
  • Anomaly Detection Techniques and Applications

Best Publications

  • Computer vision and deep learning–based data anomaly detection method for structural health monitoring:

    Yuequan Bao;Zhiyi Tang;Hui Li;Yufeng Zhang

  • The State of the Art of Data Science and Engineering in Structural Health Monitoring

    Yuequan Bao;Zhicheng Chen;Shiyin Wei;Yang Xu

  • Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

    Zhiyi Tang;Zhicheng Chen;Yuequan Bao;Hui Li

  • Machine learning paradigm for structural health monitoring

    Yuequan Bao;Hui Li

  • Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images:

    Yang Xu;Yuequan Bao;Jiahui Chen;Wangmeng Zuo

  • Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network

    Yang Xu;Yang Xu;Shiyin Wei;Shiyin Wei;Yuequan Bao;Yuequan Bao;Hui Li;Hui Li

  • Compressive sampling for accelerometer signals in structural health monitoring

    Yuequan Bao;James L Beck;Hui Li

  • Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring

    Yuequan Bao;Hui Li;Xiaodan Sun;Yan Yu

  • Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

    Zilong Zou;Yuequan Bao;Hui Li;Billie F. Spencer

  • Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio

    Shunlong Li;Shiyin Wei;Yuequan Bao;Hui Li

  • Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations

    Yuequan Bao;Yuequan Bao;Zuoqiang Shi;James L. Beck;Hui Li

  • Compressive-Sensing Data Reconstruction for Structural Health Monitoring: A Machine-Learning Approach

    Yuequan Bao;Zhiyi Tang;Hui Li

  • An active learning method combining deep neural network and weighted sampling for structural reliability analysis

    Zhengliang Xiang;Jiahui Chen;Yuequan Bao;Hui Li

  • Selection of regularization parameter for l1-regularized damage detection

    Rongrong Hou;Yong Xia;Yuequan Bao;Xiaoqing Zhou

  • Fractal Dimension‐Based Damage Detection Method for Beams with a Uniform Cross‐Section

    Hui Li;Yong Huang;Jinping Ou;Yuequan Bao

  • Structural damage identification based on integration of information fusion and shannon entropy

    Hui Li;Yuequan Bao;Jinping Ou;Jinping Ou

  • Optimal policy for structure maintenance: A deep reinforcement learning framework

    Shiyin Wei;Yuequan Bao;Hui Li

  • Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology

    Zhicheng Chen;Hui Li;Yuequan Bao;Na Li

  • Compressive sensing‐based lost data recovery of fast‐moving wireless sensing for structural health monitoring

    Yuequan Bao;Yan Yu;Hui Li;Xingquan Mao

  • Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach:

    Zhicheng Chen;Hui Li;Yuequan Bao

Frequent Co-Authors

Hui Li
Hui Li Harbin Institute of Technology
Jinping Ou
Jinping Ou Harbin Institute of Technology
Billie F. Spencer
Billie F. Spencer University of Illinois at Urbana-Champaign
James L. Beck
James L. Beck California Institute of Technology
Yong Xia
Yong Xia Hong Kong Polytechnic University
Wangmeng Zuo
Wangmeng Zuo Harbin Institute of Technology
Songye Zhu
Songye Zhu Hong Kong Polytechnic University
Thomas Y. Hou
Thomas Y. Hou California Institute of Technology
You-Lin Xu
You-Lin Xu Hong Kong Polytechnic University

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