Comparative study of damage identification algorithms applied to a bridge: II. Numerical study

Farrar, Charles R.; Jauregui, David A

doi:10.1088/0964-1726/7/5/014

Cited by 114 publications

(52 citation statements)

References 3 publications

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“…In this regard, Farrar et al [18,19] conducted comparative studies to demonstrate major limitations related to the use of modal data for damage detection. and Bayesian Information Criterion (BIC) can be found in [9,11].…”

Section: Related Workmentioning

confidence: 99%

An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

2018

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Section: Related Workmentioning

confidence: 99%

An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

2018

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“…However, Objective Function II is mainly related to modal flexibility. The coefficients of the modal flexibility matrix have been recognized as being very sensitive to damage [50]. The error gets amplified as the values of frequencies and mode shapes having noise get multiplied with each other.…”

Section: Simulated Simply Supported Beam 1 (Case 1)mentioning

confidence: 99%

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

et al. 2017

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Dynamic properties such as natural frequencies and mode shapes are directly affected by damage in structures. In this paper, changes in natural frequencies and mode shapes were used as the input to various objective functions for damage detection. Objective functions related to natural frequencies, mode shapes, modal flexibility and modal strain energy have been used, and their performances have been analyzed in varying noise conditions. Three beams were analyzed: two of which were simulated beams with single and multiple damage scenarios and one was an experimental beam. In order to do this, SAP 2000 (v14, Computers and Structures Inc., Berkeley, CA, United States, 2009) is linked with MATLAB (r2015, The MathWorks, Inc., Natick, MA, United States, 2015). The genetic algorithm (GA), an evolutionary algorithm (EA), was used to update the damaged structure for damage detection. Due to the degradation of the performance of objective functions in varying noisy conditions, a modified objective function based on the concept of regularization has been proposed, which can be effectively used in combination with EA. All three beams were used to validate the proposed procedure. It has been found that the modified objective function gives better results even in noisy and actual experimental conditions.

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“…Farrar and Jaurequi [95,96] conducted a comprehensive experimental and numerical study of different methods of damage detection (i.e., damage index, curvature mode shape, change in flexibility, change in ULS curvature, and change in stiffness) on the interstate 40 bridge.…”

Section: Literature On Comparative Studymentioning

confidence: 99%

Vibration-based Damage Identification Methods: A Review and Comparative Study

Fan

Qiao

2010

Structural Health Monitoring

1,628

215

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A comprehensive review on modal parameter-based damage identification methods for beam-or plate-type structures is presented, and the damage identification algorithms in terms of signal processing are particularly emphasized. Based on the vibration features, the damage identification methods are classified into four major categories: natural frequency-based methods, mode shape-based methods, curvature mode shape-based methods, and methods using both mode shapes and frequencies, and their merits and drawbacks are discussed. It is observed that most mode shape-based and curvature mode shape-based methods only focus on damage localization. In order to precisely locate the damage, the mode shape-based methods have to rely on optimization algorithms or signal processing techniques; while the curvature mode shape-based methods are in general a very effective type of damage localization algorithms. As an implementation, a comparative study of five extensively-used damage detection algorithms for beam-type structures is conducted to evaluate and demonstrate the validity and effectiveness of the signal processing algorithms. This brief review aims to help the readers in identifying starting points for research in vibration-based damage identification and structural health monitoring and guides researchers and practitioners in better implementing available damage identification algorithms and signal processing methods for beam-or plate-type structures.

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Comparative study of damage identification algorithms applied to a bridge: II. Numerical study

Cited by 114 publications

References 3 publications

An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

Structural Damage Detection with Different Objective Functions in Noisy Conditions Using an Evolutionary Algorithm

Vibration-based Damage Identification Methods: A Review and Comparative Study

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