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D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
48
Citations
7760
World Ranking
1314
National Ranking
527

Michael D. Todd 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 Michael D. Todd 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: 303 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Michael D. Todd 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 Michael D. Todd 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: 48 D-Index — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2004 - Hellman Fellow

Overview

Michael D. Todd is affiliated with the University of California, San Diego in the United States and has a research focus within the broad field of Engineering. Their work spans several subfields including Civil and Structural Engineering, Statistics, Probability and Uncertainty, Mechanical Engineering, Mechanics of Materials, and Control and Systems Engineering.

Their research topics cover a range of areas with emphasis on Structural Health Monitoring Techniques, Probabilistic and Robust Engineering Design, Infrastructure Maintenance and Monitoring, Concrete Corrosion and Durability, Non-Destructive Testing Techniques, Ultrasonics and Acoustic Wave Propagation, and Model Reduction and Neural Networks.

Michael D. Todd has published numerous papers in numerous scholarly venues. Frequent publication venues include:

  • Mechanical Systems and Signal Processing
  • Structural Health Monitoring
  • Structural and Multidisciplinary Optimization
  • Reliability Engineering & System Safety
  • Journal of Civil Structural Health Monitoring

Some of their recent papers are:

  • A comprehensive review of digital twin-part 1: modeling and twinning enabling technologies, 2022, Structural and Multidisciplinary Optimization
  • A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives, 2022, Structural and Multidisciplinary Optimization
  • Damage detection in railway bridges using traffic-induced dynamic responses, 2021, Engineering Structures
  • A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates, 2020, Structural Health Monitoring
  • Arbitrary polynomial chaos expansion method for uncertainty quantification and global sensitivity analysis in structural dynamics, 2020, Mechanical Systems and Signal Processing

Frequent co-authors include:

  • Zhen Hu (48 publications)
  • Mayank Chadha (19 publications)
  • Manuel A. Vega (16 publications)
  • Hua-Ping Wan (12 publications)
  • David Najera-Flores (12 publications)

Michael D. Todd was recognized as a Hellman Fellow in 2004.

Best Publications

  • Energy Harvesting for Structural Health Monitoring Sensor Networks

    Gyuhae Park;Tajana Rosing;Michael D. Todd;Charles R. Farrar

  • A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing

    Eric B. Flynn;Michael D. Todd

  • Development of an impedance-based wireless sensor node for structural health monitoring

    David L Mascarenas;Michael D Todd;Gyuhae Park;Charles R Farrar

  • A review of nonlinear dynamics applications to structural health monitoring

    Keith Worden;Charles R. Farrar;Jonathan Haywood;Michael Todd

  • Experimental studies of using wireless energy transmission for powering embedded sensor nodes

    David L. Mascarenas;Eric B. Flynn;Michael D. Todd;Timothy G. Overly

  • Maximum-likelihood estimation of damage location in guided-wave structural health monitoring

    Eric B. Flynn;Michael D. Todd;Paul D. Wilcox;Bruce W. Drinkwater

  • Nonlinear System Identification for Damage Detection

    Charles R. Farrar;Keith Worden;Michael D. Todd;Gyuhae Park

  • Vibration-based damage assessment utilizing state space geometry changes: local attractor variance ratio

    M D Todd;J M Nichols;L M Pecora;L N Virgin

  • Sensing Network Paradigms for Structural Health Monitoring

    C. R. Farrar;G. Park;M. D. Todd

  • Use of chaotic excitation and attractor property analysis in structural health monitoring

    J. M. Nichols;M. D. Todd;M. Seaver;L. N. Virgin

  • Isogeometric Fatigue Damage Prediction in Large-Scale Composite Structures Driven by Dynamic Sensor Data

    Y. Bazilevs;X. Deng;A. Korobenko;F. Lanza di Scalea

  • A mobile-agent-based wireless sensing network for structural monitoring applications

    Stuart G Taylor;Kevin M Farinholt;Eric B Flynn;Eloi Figueiredo

  • A Mobile Host Approach for Wireless Powering and Interrogation of Structural Health Monitoring Sensor Networks

    D. Mascarenas;E. Flynn;C. Farrar;G. Park

  • Bragg grating-based fibre optic sensors in structural health monitoring.

    Michael D Todd;Jonathan M Nichols;Stephen T Trickey;Mark Seaver

  • Optimal Placement of Piezoelectric Actuators and Sensors for Detecting Damage in Plate Structures

    Eric B. Flynn;Michael D. Todd

  • Using state space predictive modeling with chaotic interrogation in detecting joint preload loss in a frame structure experiment

    J M Nichols;M D Todd;J R Wait

  • Damage detection in railway bridges using traffic-induced dynamic responses

    Andreia Meixedo;João Santos;Diogo Ribeiro;Rui Calçada

  • Structural Health Monitoring Through Chaotic Interrogation

    J. M. Nichols;S. T. Trickey;M. D. Todd;L. N. Virgin

  • Deployment of a fiber Bragg grating-based measurement system in a structural health monitoring application

    M D Todd;G A Johnson;S T Vohra

  • A multivariate, attractor-based approach to structural health monitoring

    L. Moniz;J.M. Nichols;C.J. Nichols;M. Seaver

  • A mobile-agent based wireless sensing network for structural monitoring applications

    Stuart G Taylor;Kevin M Farinholt;Eloi Figueiredo;Gyuhae Park

Frequent Co-Authors

Gyuhae Park
Gyuhae Park Chonnam National University
Charles R. Farrar
Charles R. Farrar Los Alamos National Laboratory
Matthew Barth
Matthew Barth University of California, Riverside
Lawrence N. Virgin
Lawrence N. Virgin Duke University
Paul D. Wilcox
Paul D. Wilcox University of Bristol
Bruce W. Drinkwater
Bruce W. Drinkwater University of Bristol
Keith Worden
Keith Worden University of Sheffield
Tajana Rosing
Tajana Rosing University of California, San Diego
Susan Shaheen
Susan Shaheen University of California, Berkeley
Rajesh Gupta
Rajesh Gupta University of California, San Diego

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