World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
48
Citations
8225
World Ranking
4610
National Ranking
1329

Linbing Wang 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 Linbing Wang sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 134 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: 117 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: 59 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: 326 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

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

Linbing Wang 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 Linbing Wang sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 128 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: 349 scientists 41 D-Index: 362 scientists 42 D-Index: 425 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: 94 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: 24 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: 48 D-Index — 55th percentile

55% of scientists in this discipline score the same or lower.

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

Overview

Linbing Wang is a researcher affiliated with Virginia Tech in the United States, focusing broadly on engineering with a strong emphasis on civil and structural engineering. Their research output includes considerable work in subfields such as mechanical engineering, building and construction, mechanics of materials, and biomedical engineering.

Their scholarly contributions cover multiple specialized topics, including:

  • Infrastructure Maintenance and Monitoring
  • Asphalt Pavement Performance Evaluation
  • Transport Systems and Technology
  • Innovative Concrete Reinforcement Materials
  • Concrete and Cement Materials Research
  • Structural Health Monitoring Techniques
  • Geotechnical Engineering and Underground Structures

Linbing Wang has published extensively in peer-reviewed journals, with frequent publication venues that highlight a consistent focus on applied materials and construction technologies. Notable journals featuring their work include:

  • Construction and Building Materials
  • Materials
  • Sensors
  • Applied Sciences
  • SSRN Electronic Journal

Among recent publications, several papers address applications of sensing technologies, machine learning, and image processing in pavement monitoring and analysis:

  • The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis (2020, Engineering)
  • UNet-based model for crack detection integrating visual explanations (2022, Construction and Building Materials)
  • Deep learning and infrared thermography for asphalt pavement crack severity classification (2022, Automation in Construction)
  • Microstructural characteristics and their impact on mechanical properties of steel-PVA fiber reinforced concrete (2021, Cement and Concrete Composites)
  • Asphalt Pavement Crack Detection Based on Convolutional Neural Network and Infrared Thermography (2022, IEEE Transactions on Intelligent Transportation Systems)

The frequent co-authors collaborating with Linbing Wang demonstrate a network of research partnerships with scholars active in similar research domains. These include:

  • Zhoujing Ye
  • Hailu Yang
  • Ya Wei
  • Yajian Wang
  • Yinghao Miao

Linbing Wang's research profile is characterized by a focus on advancing knowledge in infrastructure monitoring through integration of cutting-edge sensing, imaging, and data analysis methods.

Best Publications

  • The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis

    Yue Hou;Qiuhan Li;Qiuhan Li;Chen Zhang;Chen Zhang;Guoyang Lu

  • Piezoelectric energy harvester for public roadway: On-site installation and evaluation

    Haocheng Xiong;Linbing Wang

  • Representation of real particles for DEM simulation using X-ray tomography

    Linbing Wang;Jin-Young Park;Yanrong Fu

  • UNet-based model for crack detection integrating visual explanations

    Unknown

  • History of Hot Mix Asphalt Mixture Design in the United States

    Freddy L. Roberts;Louay N. Mohammad;L. B. Wang

  • Fracture Resistance Characterization of Superpave Mixtures Using the Semi-Circular Bending Test

    Zhong Wu;Louay N Mohammad;L B Wang;Mary Ann Mull

  • Unified Method to Quantify Aggregate Shape Angularity and Texture Using Fourier Analysis

    Linbing Wang;Xingran Wang;Louay Mohammad;Chris Abadie

  • Three-Dimensional Image Analysis of Aggregate Particles from Orthogonal Projections

    C.-Y. Kuo;J. D. Frost;J. S. Lai;L. B. Wang

  • Three-Dimensional Digital Representation of Granular Material Microstructure from X-Ray Tomography Imaging

    L. B. Wang;J. D. Frost;J. S. Lai

  • A field trial of horizontal jet grouting using the composite-pipe method in the soft deposits of Shanghai

    Shui-Long Shen;Zhi-Feng Wang;Wen-Juan Sun;Lin-Bing Wang

  • MICROSTRUCTURE STUDY OF WESTRACK MIXES FROM X-RAY TOMOGRAPHY IMAGES

    L. B. Wang;J. D. Frost;Naga Shashidhar

  • A review and perspective for research on moisture damage in asphalt pavement induced by dynamic pore water pressure

    Wentao Wang;Linbing Wang;Linbing Wang;Haocheng Xiong;Rong Luo

  • Quantification of damage parameters using X-ray tomography images

    L.B. Wang;J.D. Frost;G.Z Voyiadjis;T.P. Harman

  • Deep learning and infrared thermography for asphalt pavement crack severity classification

    Unknown

  • Mechanics of Asphalt: Microstructure and Micromechanics

    Linbing Wang

  • A direct characterization of interfacial interaction between asphalt binder and mineral fillers by atomic force microscopy

    Meng Guo;Yiqiu Tan;Jianxin Yu;Yue Hou

  • Micromechanics Study on Top-Down Cracking:

    L B Wang;L A Myers;L N Mohammad;Y R Fu

  • Anisotropic Properties of Asphalt Concrete: Characterization and Implications for Pavement Design and Analysis

    Linbing Wang;Laureano R. Hoyos;Jay Wang;George Voyiadjis

  • Microstructural characteristics and their impact on mechanical properties of steel-PVA fiber reinforced concrete

    Fangyu Liu;Ke Xu;Ke Xu;Wenqi Ding;Wenqi Ding;Yafei Qiao;Yafei Qiao

  • Understanding the relationships between rheology and chemistry of asphalt binders: A review

    Unknown

  • Effect of Basalt Fiber on the Asphalt Binder and Mastic at Low Temperature

    Dong Wang;Linbing Wang;Xinyu Gu;Guoqing Zhou

  • Asphalt Pavement Crack Detection Based on Convolutional Neural Network and Infrared Thermography

    Unknown

  • Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning

    Unknown

  • Microstructure Characterization for Modeling HMA Behaviour Using Imaging Technology

    Laith Tashman;Linbing Wang;Senthil Thyagarajan

  • Investigation of the Asphalt Self-Healing Mechanism Using a Phase-Field Model

    Yue Hou;Linbing Wang;Troy Pauli;Wenjuan Sun

  • Integration of GIS and Data Mining Technology to Enhance the Pavement Management Decision Making

    Guoqing Zhou;Linbing Wang;Dong Wang;Scott Reichle

  • Characterization of Bitumen Micro-Mechanical Behaviors Using AFM, Phase Dynamics Theory and MD Simulation.

    Yue Hou;Linbing Wang;Dawei Wang;Meng Guo

  • Diffusion of asphaltene, resin, aromatic and saturate components of asphalt on mineral aggregates surface: molecular dynamics simulation

    Meng Guo;Yiqiu Tan;Linbing Wang;Yue Hou

  • Optimizing asphalt mix design through predicting the rut depth of asphalt pavement using machine learning

    Unknown

  • Development in Stacked-Array-Type Piezoelectric Energy Harvester in Asphalt Pavement

    Hailu Yang;Linbing Wang;Yue Hou;Meng Guo

  • Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation

    Unknown

  • A review on pavement distress and structural defects detection and quantification technologies using imaging approaches

    Unknown

Frequent Co-Authors

Yiqiu Tan
Yiqiu Tan Harbin Institute of Technology
Louay N. Mohammad
Louay N. Mohammad Louisiana State University
Markus Oeser
Markus Oeser RWTH Aachen University
Erol Tutumluer
Erol Tutumluer University of Illinois at Urbana-Champaign
Xiaoming Huang
Xiaoming Huang Southeast University
Baoshan Huang
Baoshan Huang University of Tennessee at Knoxville
George Z. Voyiadjis
George Z. Voyiadjis Louisiana State University
Shihui Shen
Shihui Shen Pennsylvania State University
Shui-Long Shen
Shui-Long Shen Shantou University
Takaki Komiyama
Takaki Komiyama University of California, San Diego

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