World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
49
Citations
7353
World Ranking
4416
National Ranking
875

Tak-Ming Chan 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 Tak-Ming Chan 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: 269 publications — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Tak-Ming Chan 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 Tak-Ming Chan 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: 49 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Overview

Tak-Ming Chan is affiliated with Hong Kong Polytechnic University in China and specializes in engineering, with a focus on civil and structural engineering. Their research covers building and construction, mechanics of materials, mechanical engineering, and materials chemistry. The primary emphasis lies in areas related to structural load-bearing analysis and reinforced concrete behavior.

The scientist's work addresses key topics such as:

  • Structural Load-Bearing Analysis
  • Structural Behavior of Reinforced Concrete
  • Fire effects on concrete materials
  • Structural Engineering and Vibration Analysis
  • Concrete Corrosion and Durability
  • Innovative concrete reinforcement materials
  • Seismic Performance and Analysis

Tak-Ming Chan has contributed extensively to leading publication venues, frequently appearing in:

  • Thin-Walled Structures
  • Engineering Structures
  • Journal of Constructional Steel Research
  • Structures
  • Journal of Structural Engineering

Recent published papers include:

  • Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading, 2022, Engineering Structures
  • Material properties and residual stresses of cold-formed high-strength-steel circular hollow sections, 2020, Journal of Constructional Steel Research
  • Effect of inter-module connections on progressive collapse behaviour of MiC structures, 2021, Journal of Constructional Steel Research
  • Experimental investigation on recycled aggregate concrete filled steel tubular stub columns under axial compression, 2021, Journal of Constructional Steel Research
  • Cold-formed high strength steel tubular beam-columns, 2020, Engineering Structures

Throughout their career, Tak-Ming Chan has collaborated frequently with several co-authors, including:

  • Ben Young
  • Junbo Chen
  • Jun-zhi Liu
  • Han Fang
  • Jiachen Guo

Tak-Ming Chan's research contributes largely to understanding the mechanical and structural behavior of steel and concrete materials, as well as developing predictive models to improve the evaluation of structural components under various loading and environmental conditions.

Best Publications

  • The Landscape of MicroRNA, Piwi-Interacting RNA, and Circular RNA in Human Saliva

    Jae Hoon Bahn;Qing Zhang;Feng Li;Tak-Ming Chan

  • Compressive resistance of hot-rolled elliptical hollow sections

    Tak Ming Chan;L. Gardner

  • Structural response of stainless steel oval hollow section compression members

    M. Theofanous;Tak-Ming Chan;L. Gardner

  • Neighbor embedding based super-resolution algorithm through edge detection and feature selection

    Tak-Ming Chan;Junping Zhang;Jian Pu;Hua Huang

  • Bending strength of hot-rolled elliptical hollow sections

    Tak Ming Chan;L. Gardner

  • Material properties and residual stresses of cold-formed high strength steel hollow sections

    Jia Lin Ma;Tak Ming Chan;Ben Young

  • Experimental investigation on stub-column behavior of cold-formed high-strength steel tubular sections

    Jia-Lin Ma;Tak-Ming Chan;Ben Young

  • Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification.

    Yong Liang;Cheng Liu;Xin-Ze Luan;Kwong-Sak Leung

  • Flexural Buckling of Elliptical Hollow Section Columns

    Tak Ming Chan;Tak Ming Chan;L. Gardner;L. Gardner

  • Cross-section classification of elliptical hollow sections

    L. Gardner;T. M. Chan

  • Flexural behaviour of stainless steel oval hollow sections

    M. Theofanous;Tak Ming Chan;L. Gardner

  • Structural response of concrete-filled elliptical steel hollow sections under eccentric compression

    Therese Sheehan;Xianghe Dai;T.M. Chan;Dennis Lam

  • A study of hybrid self-centring connections equipped with shape memory alloy washers and bolts

    Cheng Fang;Michael C.H. Yam;Tak-Ming Chan;Wei Wang

  • Civil and structural engineering applications, recent trends, research and developments on pultruded fiber reinforced polymer closed sections: a review

    Alfred Kofi Gand;Tak Ming Chan;James Toby Mottram

  • Structural design of elliptical hollow sections: a review

    Tak-Ming Chan;L. Gardner;K. H. Law

  • Experimental investigation on lightweight concrete-filled cold-formed elliptical hollow section stub columns

    Tak-Ming Chan;Yun-Mei Huai;Wei Wang

  • Experimental investigation on octagonal concrete filled steel stub columns under uniaxial compression

    Jiong Yi Zhu;Tak Ming Chan

  • Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading

    Unknown

  • Behaviour of concrete-filled cold-formed elliptical hollow sections with varying aspect ratios

    Faqi Liu;Yuyin Wang;Tak-ming Chan

  • Tensile behaviour of concrete-filled double-skin steel tubular members

    Wei Li;Lin Hai Han;Tak Ming Chan

  • Cyclic behavior of connections equipped with NiTi shape memory alloy and steel tendons between H-shaped beam to CHS column

    Wei Wang;Tak-Ming Chan;Hongliang Shao;Yiyi Chen

  • Experimental investigation of cold-formed high strength steel tubular beams

    Jia-Lin Ma;Tak-Ming Chan;Ben Young

  • Numerical investigation on the performance of concrete-filled double-skin steel tubular members under tension

    Wei Li;Lin-Hai Han;Tak-Ming Chan

Frequent Co-Authors

Ben Young
Ben Young Hong Kong Polytechnic University
Kwong-Sak Leung
Kwong-Sak Leung Chinese University of Hong Kong
Leroy Gardner
Leroy Gardner Imperial College London
Yong Liang
Yong Liang Jianghan University
Zongben Xu
Zongben Xu Xi'an Jiaotong University
Lin-Hai Han
Lin-Hai Han Tsinghua University
Kwok-Fai Chung
Kwok-Fai Chung Hong Kong Polytechnic University
Dennis Lam
Dennis Lam University of Bradford
Stephen Kwok-Wing Tsui
Stephen Kwok-Wing Tsui Chinese University of Hong Kong
Wei Li
Wei Li Tianjin Polytechnic University

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