World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
49
Citations
7545
World Ranking
1244
National Ranking
506

Yongming Liu publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Yongming Liu sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 270 publications — 65th percentile

65% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Yongming Liu D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Yongming Liu sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 49 D-Index — 65th percentile

65% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Overview

Yongming Liu is affiliated with Arizona State University in the United States and has contributed extensively to the field of engineering. Their research covers a wide range of engineering disciplines, focusing primarily on aerospace engineering, civil and structural engineering, mechanical engineering, mechanics of materials, and artificial intelligence.

The main topics addressed in Yongming Liu's work include:

  • Air Traffic Management and Optimization
  • Structural Health Monitoring Techniques
  • Fatigue and fracture mechanics
  • Probabilistic and Robust Engineering Design
  • Non-Destructive Testing Techniques
  • Autonomous Vehicle Technology and Safety
  • Aerospace and Aviation Technology

Liu has published numerous papers with some notable recent works as follows:

  • Removal of Copper Ions from Wastewater: A Review, 2023, International Journal of Environmental Research and Public Health
  • Fatigue modeling using neural networks: A comprehensive review, 2022, Fatigue & Fracture of Engineering Materials & Structures
  • Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach, 2021, Transportation Research Part C Emerging Technologies
  • Probabilistic physics-guided machine learning for fatigue data analysis, 2020, Expert Systems with Applications
  • Structural dynamics simulation using a novel physics-guided machine learning method, 2020, Engineering Applications of Artificial Intelligence

Frequent collaborators in Liu's research include:

  • Yutian Pang
  • Jueming Hu
  • Changyu Meng
  • Yi Gao
  • Hao Yan

Liu's work has appeared regularly in several scholarly venues, demonstrating a pattern of publication in:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • AIAA Scitech 2020 Forum
  • International Journal of Fatigue
  • Reliability Engineering & System Safety

Additionally, Yongming Liu has contributed to book publications, including a title published by Springer Science+Business Media:

  • Problem Solving Methods and Strategies in High School Mathematical Competitions, 2023

Best Publications

  • Probabilistic fatigue life prediction using an equivalent initial flaw size distribution

    Yongming Liu;Sankaran Mahadevan

  • Multiaxial high-cycle fatigue criterion and life prediction for metals

    Yongming Liu;Sankaran Mahadevan

  • Microstructure Representation and Reconstruction of Heterogeneous Materials Via Deep Belief Network for Computational Material Design

    Ruijin Cang;Yaopengxiao Xu;Shaohua Chen;Yongming Liu

  • Fatigue crack initiation life prediction of railroad wheels

    Yongming Liu;Brant Stratman;Sankaran Mahadevan

  • Probabilistic physics-guided machine learning for fatigue data analysis

    Unknown

  • Probabilistic prediction with Bayesian updating for strength degradation of RC bridge beams

    Yafei Ma;Jianren Zhang;Lei Wang;Yongming Liu

  • Stochastic fatigue damage modeling under variable amplitude loading

    Yongming Liu;Sankaran Mahadevan

  • Structural health monitoring of railroad wheels using wheel impact load detectors

    Brant Stratman;Yongming Liu;Sankaran Mahadevan

  • Structural response reconstruction based on empirical mode decomposition in time domain

    Jingjing He;Xuefei Guan;Yongming Liu

  • In-situ fatigue life prognosis for composite laminates based on stiffness degradation

    Tishun Peng;Yongming Liu;Abhinav Saxena;Kai Goebel

  • A probabilistic crack size quantification method using in-situ Lamb wave test and Bayesian updating

    Jinsong Yang;Jingjing He;Xuefei Guan;Dengjiang Wang

  • Analysis of subsurface crack propagation under rolling contact loading in railroad wheels using FEM

    Yongming Liu;Liming Liu;Sankaran Mahadevan

  • A unified multiaxial fatigue damage model for isotropic and anisotropic materials

    Yongming Liu;Sankaran Mahadevan

  • Fatigue life prediction for aging RC beams considering corrosive environments

    Yafei Ma;Yibing Xiang;Lei Wang;Jianren Zhang

  • Threshold stress intensity factor and crack growth rate prediction under mixed-mode loading

    Yongming Liu;Sankaran Mahadevan

  • Crack growth-based fatigue life prediction using an equivalent initial flaw model. Part I: Uniaxial loading

    Yibing Xiang;Zizi Lu;Yongming Liu

  • A generalized 2D non-local lattice spring model for fracture simulation

    Hailong Chen;Enqiang Lin;Yang Jiao;Yongming Liu

  • Model selection, updating, and averaging for probabilistic fatigue damage prognosis

    Xuefei Guan;Ratneshwar Jha;Yongming Liu

  • A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves

    Jingjing He;Xuefei Guan;Tishun Peng;Yongming Liu

  • Structural dynamics simulation using a novel physics-guided machine learning method

    Yang Yu;Houpu Yao;Yongming Liu

  • Investigation of incremental fatigue crack growth mechanisms using in situ SEM testing

    Wei Zhang;Yongming Liu

  • Small time scale fatigue crack growth analysis

    Zizi Lu;Yongming Liu

Frequent Co-Authors

Sankaran Mahadevan
Sankaran Mahadevan Vanderbilt University
Kai Goebel
Kai Goebel Palo Alto Research Center
Abhinav Saxena
Abhinav Saxena General Electric (United States)
Jun He
Jun He University of Nottingham Ningbo China
Lei Ying
Lei Ying University of Michigan–Ann Arbor
Steve C.S. Cai
Steve C.S. Cai Southeast University
Alexander A. Green
Alexander A. Green Boston University
Nima Shamsaei
Nima Shamsaei Auburn University
Nancy J. Cooke
Nancy J. Cooke Arizona State University
R. Srikant
R. Srikant University of Illinois at Urbana-Champaign

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