World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
84
Citations
40114
World Ranking
398
National Ranking
138

Overview

Charles R. Farrar is affiliated with the Los Alamos National Laboratory in the United States. Their research spans multiple areas within engineering and decision sciences, with significant contributions to structural health monitoring and probabilistic approaches to engineering design.

The primary fields of study covered in their work include Engineering and Decision Sciences. Subfields of their expertise notably include Civil and Structural Engineering, Statistics, Probability and Uncertainty, Control and Systems Engineering, Computer Vision and Pattern Recognition, and Atomic and Molecular Physics, and Optics. Their research topics focus on Structural Health Monitoring Techniques, Probabilistic and Robust Engineering Design, Risk and Safety Analysis, Infrastructure Maintenance and Monitoring, Concrete Corrosion and Durability, Fault Detection and Control Systems, and Ultrasonics and Acoustic Wave Propagation.

Frequent coauthors collaborating with Charles R. Farrar are Michael D. Todd, Keith Worden, David Mascareñas, Mayank Chadha, and Zhen Hu.

The venues where their work has been published include Mechanical Systems and Signal Processing, Strain, Engineering Structures, Nonlinear Dynamics, and Earthquake Engineering & Structural Dynamics.

Selected recent papers by Charles R. Farrar and research related to their field include:

  • "A probabilistic risk-based decision framework for structural health monitoring", 2020, Mechanical Systems and Signal Processing
  • "Blind, simultaneous identification of full-field vibration modes and large rigid-body motion of output-only structures from digital video measurements", 2020, Engineering Structures
  • "The Past, Present and Future of Structural Health Monitoring: An Overview of Three Ages", 2025, Strain
  • "Connecting mem-models with classical theories", 2021, Nonlinear Dynamics
  • "Efficient regional seismic risk assessment via deep generative learning of surrogate models", 2023, Earthquake Engineering & Structural Dynamics

Best Publications

  • Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review

    Scott W. Doebling;Charles R. Farrar;Michael B. Prime;D.W. Shevitz

  • A summary review of vibration-based damage identification methods

    S. W. Doebling;C. R. Farrar;M. B. Prime

  • An introduction to structural health monitoring

    Charles R Farrar;Keith Worden

  • Structural Health Monitoring: A Machine Learning Perspective

    Charles R. Farrar;Keith Worden

  • Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward

    Gyuhae Park;Hoon Sohn;Charles R. Farrar;Daniel J. Inman

  • Vibration–based structural damage identification

    Charles R. Farrar;Scott W. Doebling;David A. Nix

  • A review of structural health monitoring literature 1996-2001

    Charles R. Farrar;Jerry J. Czarnecki;Hoon Sohn;Francois M. Hemez

  • Damage diagnosis using time series analysis of vibration signals

    Hoon Sohn;Charles R Farrar

  • The fundamental axioms of structural health monitoring

    Keith Worden;Charles R Farrar;Graeme Manson;Gyuhae Park

  • APPLICATION OF THE STRAIN ENERGY DAMAGE DETECTION METHOD TO PLATE-LIKE STRUCTURES

    P. Cornwell;S.W. Doebling;C.R. Farrar

  • Comparative study of damage identification algorithms applied to a bridge: I. Experiment

    Charles R Farrar;David A Jauregui

  • Variability of Modal Parameters Measured on the Alamosa Canyon Bridge

    C. R. Farrar;S. W. Doebling;P. J. Cornwell;E. G. Straser

  • SYSTEM IDENTIFICATION FROM AMBIENT VIBRATION MEASUREMENTS ON A BRIDGE

    C.R. Farrar;G.H. James

  • Structural Health Monitoring Using Statistical Pattern Recognition Techniques

    Hoon Sohn;Charles R. Farrar;Norman F. Hunter;Keith Worden

  • Energy Harvesting for Structural Health Monitoring Sensor Networks

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

  • Damage prognosis: the future of structural health monitoring

    Charles R Farrar;Nick A.J Lieven

  • Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande

    C.R. Farrar;W.E. Baker;T.M. Bell;K.M. Cone

  • Structural Health Monitoring Using Statistical Process Control

    Hoon Sohn;Jerry A. Czarnecki;Charles R. Farrar

  • Machine learning algorithms for damage detection under operational and environmental variability

    Eloi Figueiredo;Gyuhae Park;Charles R Farrar;Keith Worden

  • Time reversal active sensing for health monitoring of a composite plate

    Hyun Woo Park;Hoon Sohn;Kincho H. Law;Charles R. Farrar

  • A Review of Structural Health Review of Structural Health Monitoring Literature 1996-2001.

    Hoon Sohn;Charles R. Farrar;François M. Hemez;Jerry J. Czarnecki

Frequent Co-Authors

Gyuhae Park
Gyuhae Park Chonnam National University
Hoon Sohn
Hoon Sohn Korea Advanced Institute of Science and Technology
Keith Worden
Keith Worden University of Sheffield
Michael D. Todd
Michael D. Todd University of California, San Diego
Kincho H. Law
Kincho H. Law Stanford University
Jung-Ryul Lee
Jung-Ryul Lee Korea Advanced Institute of Science and Technology
Jerome P. Lynch
Jerome P. Lynch University of Michigan–Ann Arbor
Gautam Gupta
Gautam Gupta University of Louisville
Tajana Rosing
Tajana Rosing University of California, San Diego
Aditya D. Mohite
Aditya D. Mohite Rice University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Best Scientists Citing Charles R. Farrar

Trending Scientists

Recently Published Articles