World's Best Scientists 2026 revealed!
Wengang Zhang

Wengang Zhang

D-Index & Metrics

Engineering and Technology

D-Index
59
Citations
14194
World Ranking
2327
National Ranking
465

Wengang Zhang 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 Wengang Zhang 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: 345 publications — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Wengang Zhang 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 Wengang Zhang 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

Wengang Zhang is a researcher affiliated with Chongqing University in China specializing in engineering with a focus on civil and structural engineering. Their work spans areas including safety, risk, reliability and quality, as well as management, monitoring, policy and law within the engineering domain.

Their main research topics cover geotechnical engineering and analysis, landslides and related hazards, dam engineering and safety, geotechnical engineering related to underground structures, rock mechanics and modeling, geotechnical engineering and soil stabilization, and grouting, rheology, and soil mechanics.

Zhang's frequent publication venues include Underground Space, Geoscience Frontiers, Acta Geotechnica, Geological Journal, and Computers and Geotechnics. These outlets have featured multiple publications by Zhang, reflecting ongoing research activity in geotechnical and structural engineering fields.

Some of Zhang's recent papers include:

  • Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization, 2020, Geoscience Frontiers
  • Application of deep learning algorithms in geotechnical engineering: a short critical review, 2021, Artificial Intelligence Review
  • Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge, 2022, Gondwana Research

Zhang has collaborated often with several co-authors including Luqi Wang, Yongqin Li, Runhong Zhang, Enqiang Linghu, and Weixin Sun. These collaborations have contributed to a significant number of joint publications.

Best Publications

  • Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

    Wengang Zhang;Chongzhi Wu;Haiyi Zhong;Yongqin Li

  • Multivariate adaptive regression splines and neural network models for prediction of pile drivability

    Wengang Zhang;Anthony Teck Chee Goh

  • State-of-the-art review of soft computing applications in underground excavations

    Wengang Zhang;Runhong Zhang;Chongzhi Wu;Anthony Teck Chee Goh

  • Application of deep learning algorithms in geotechnical engineering: a short critical review

    Wengang Zhang;Hongrui Li;Yongqin Li;Hanlong Liu

  • Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: comprehensive review and future challenge

    Unknown

  • Multivariate adaptive regression splines for analysis of geotechnical engineering systems

    Wengang Zhang;Anthony Teck Chee Goh

  • Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

    Xinzhi Zhou;Haijia Wen;Yalan Zhang;Jiahui Xu

  • Future of machine learning in geotechnics

    Unknown

  • Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method

    Lin Wang;Lin Wang;Chongzhi Wu;Libin Tang;Wengang Zhang

  • Influence of Particle Breakage on Critical State Line of Rockfill Material

    Yang Xiao;Hanlong Liu;Xuanming Ding;Yumin Chen

  • Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques

    Jian Zhou;Yingui Qiu;Danial Jahed Armaghani;Wengang Zhang

  • Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling

    W.G. Zhang;H.R. Li;C.Z. Wu;Y.Q. Li

  • Probabilistic stability analysis of earth dam slope under transient seepage using multivariate adaptive regression splines

    Lin Wang;Chongzhi Wu;Xin Gu;Hanlong Liu

  • Assessment of pile drivability using random forest regression and multivariate adaptive regression splines

    Wengang Zhang;Chongzhi Wu;Yongqin Li;Lin Wang

  • Determination of earth pressure balance tunnel-related maximum surface settlement: a multivariate adaptive regression splines approach

    Anthony Teck Chee Goh;Wengang Zhang;Wengang Zhang;Yanmei Zhang;Yang Xiao

  • A simple prediction model for wall deflection caused by braced excavation in clays

    Wengang Zhang;Anthony T.C. Goh;Feng Xuan

  • Reliability assessment on stability of tunnelling perpendicularly beneath an existing tunnel considering spatial variabilities of rock mass properties

    Fuyong Chen;Lin Wang;Wengang Zhang

  • An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost

    Unknown

  • Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines

    Wengang Zhang;Anthony T.C. Goh;Yanmei Zhang;Yumin Chen

  • Application of transparent soil model test and DEM simulation in study of tunnel failure mechanism

    Yuzhou Xiang;Hanlong Liu;Wengang Zhang;Wengang Zhang;Jian Chu

  • A Multivariate Adaptive Regression Splines model for determining horizontal wall deflection envelope for braced excavations in clays

    Wengang Zhang;Wengang Zhang;Runhong Zhang;Wei Wang;Fan Zhang

  • Reliability assessment on ultimate and serviceability limit states and determination of critical factor of safety for underground rock caverns

    Wengang Zhang;Anthony Teck Chee Goh

  • Multivariate adaptive regression splines for inverse analysis of soil and wall properties in braced excavation

    Wengang Zhang;Yanmei Zhang;Anthony T.C. Goh

Frequent Co-Authors

Anthony T. C. Goh
Anthony T. C. Goh Nanyang Technological University
Hanlong Liu
Hanlong Liu Chongqing University
Yang Xiao
Yang Xiao Chongqing University
Xuanming Ding
Xuanming Ding Chongqing University
Zhongmin Xiao
Zhongmin Xiao Nanyang Technological University
Thomas Böhlke
Thomas Böhlke Karlsruhe Institute of Technology
Pijush Samui
Pijush Samui National Institute of Technology Patna
Jian Chu
Jian Chu Nanyang Technological University
Jianye Ching
Jianye Ching National Taiwan University
Danial Jahed Armaghani
Danial Jahed Armaghani University of Technology Sydney

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