World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
37
Citations
4135
World Ranking
2453
National Ranking
181

Elizabeth J. Cross 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 Elizabeth J. Cross 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: 178 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Elizabeth J. Cross 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 Elizabeth J. Cross 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: 37 D-Index — 32nd percentile

32% of scientists in this discipline score the same or lower.

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

Best Publications

  • Long-term monitoring and data analysis of the Tamar Bridge

    E.J. Cross;K.Y. Koo;J.M.W. Brownjohn;K. Worden

  • Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data

    Elizabeth J. Cross;Keith Worden;Qian Chen

  • Features for damage detection with insensitivity to environmental and operational variations

    Elizabeth Cross;Graham Manson;Keith Worden;Stephen Pierce

  • On robust regression analysis as a means of exploring environmental and operational conditions for SHM data

    N. Dervilis;K. Worden;E.J. Cross

  • Foundations of population-based SHM, Part I : homogeneous populations and forms

    L.A. Bull;P.A. Gardner;J. Gosliga;T.J. Rogers

  • Improving aircraft performance using machine learning: A review

    Unknown

  • On switching response surface models, with applications to the structural health monitoring of bridges

    K. Worden;E.J. Cross

  • Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression

    T.J. Rogers;P. Gardner;N. Dervilis;K. Worden

  • A Bayesian non-parametric clustering approach for semi-supervised Structural Health Monitoring

    T.J. Rogers;K. Worden;R. Fuentes;N. Dervilis

  • Linear approaches to modeling nonlinearities in long-term monitoring of bridges

    Unknown

  • Damage detection in operational wind turbine blades using a new approach based on machine learning

    Kartik Chandrasekhar;Nevena Stevanovic;Elizabeth J. Cross;Nikolaos Dervilis

  • A multiresolution approach to cointegration for enhanced SHM of structures under varying conditions – An exploratory study

    K. Worden;E.J. Cross;I. Antoniadou;A. Kyprianou

  • A regime-switching cointegration approach for removing environmental and operational variations in structural health monitoring

    Haichen Shi;Keith Worden;Elizabeth J. Cross

  • Prediction of Landing Gear Loads Using Machine Learning Techniques

    Geoffrey K T Holmes;Pia N Sartor;Stephen Reed;Paul J D Southern

  • Equation discovery for nonlinear dynamical systems: A Bayesian viewpoint

    R. Fuentes;R. Nayek;P. Gardner;N. Dervilis

  • Probabilistic active learning : an online framework for structural health monitoring

    L.A. Bull;T.J. Rogers;C. Wickramarachchi;E.J. Cross

  • On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification

    T.J. Rogers;K. Worden;E.J. Cross

  • On evolutionary system identification with applications to nonlinear benchmarks

    K. Worden;R.J. Barthorpe;E.J. Cross;N. Dervilis

  • Robust methods of inclusive outlier analysis for structural health monitoring

    N. Dervilis;E.J. Cross;R.J. Barthorpe;K. Worden

  • Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling

    R. Fuentes;R.S. Dwyer-Joyce;M.B. Marshall;J. Wheals

Frequent Co-Authors

Keith Worden
Keith Worden University of Sheffield
Nikolaos Dervilis
Nikolaos Dervilis University of Sheffield
David J. Wagg
David J. Wagg University of Sheffield
Pam Enderby
Pam Enderby University of Sheffield
Charles R. Farrar
Charles R. Farrar Los Alamos National Laboratory
James M. W. Brownjohn
James M. W. Brownjohn University of Exeter
Jon Nicholl
Jon Nicholl University of Sheffield
Steven A. Julious
Steven A. Julious University of Sheffield
Simon Capewell
Simon Capewell University of Liverpool
Eleni Chatzi
Eleni Chatzi ETH Zurich

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