Structural damage detection using finite element model updating with evolutionary algorithms: a survey

Alkayem, Nizar Faisal; Cao, Maosen; Zhang, Yufeng; Bayat, Mahmoud; Su, Zhongqing

doi:10.1007/s00521-017-3284-1

Cited by 183 publications

(57 citation statements)

References 140 publications

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“…These are used for the detection of system changes; underlying changes in the parameters of the physics-based model can indicate potential structural damage. This work builds upon a surge of recent work within the model updating community (Vigliotti et al, 2018;Schommer et al, 2017;Alkayem et al, 2017). The estimation in the material properties of beams, such as flexural rigidity, has been a major area of interest in such literature.…”

Section: Discussionmentioning

confidence: 99%

“…Hybrid approaches are commonplace in literature, utilising data accrued from instrumented structures to improve predictions of structural response to stimuli from these physics-based models. A commonly used example of a data/physics hybrid approach is model updating (Rocchetta et al, 2018;Grafe;Schommer et al, 2017;Alkayem et al, 2017;Vigliotti et al, 2018). In model updating, the parameters of the physics-based model are estimated using the measured response data; predicted responses of the structure are then given by the model with the estimated parameters.…”

Section: Introductionmentioning

confidence: 99%

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The synthesis of data from instrumented structures and physics-based models via Gaussian processes

Gregory

Lau

Girolami

et al. 2019

Journal of Computational Physics

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At the heart of structural engineering research is the use of data obtained from physical structures such as bridges, viaducts and buildings. These data can represent how the structure responds to various stimuli over time when in operation. Many models have been proposed in literature to represent such data, such as linear statistical models. Based upon these models, the health of the structure is reasoned about, e.g. through damage indices, changes in likelihood and statistical parameter estimates. On the other hand, physicsbased models are typically used when designing structures to predict how the structure will respond to operational stimuli. These models represent how the structure responds to stimuli under idealised conditions. What remains unclear in the literature is how to combine the observed data with information from the idealised physics-based model into a model that describes the responses of the operational structure. This paper introduces a new approach which fuses together observed data from a physical structure during operation and information from a mathematical model. The observed data are combined with data simulated from the physics-based model using a multi-output Gaussian process formulation. The novelty of this method is how the information from observed data and the physics-based model is balanced to obtain a representative model of the structures response to stimuli. We present our method using data obtained from a fibre-optic sensor network installed on experimental railway sleepers. The curvature of the sleeper at sensor and also non-sensor locations is modelled, guided by the mathematical representation. We discuss how this approach can be used to reason about changes in the structures behaviour over time using simulations and experimental data. The results show that the methodology can accurately detect such changes. They also indicate that the methodology can infer information about changes in the parameters within the physics-based model, including those governing components of the structure not measured directly by sensors such as the ballast foundation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The synthesis of data from instrumented structures and physics-based models via Gaussian processes

Gregory

Lau

Girolami

et al. 2019

Journal of Computational Physics

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“…To tackle the structural damage estimation problem, it is essential to organize the optimization problem into two main divisions: the single-objective EAs and the MOEAs. The key points to understand the differences between the two divisions can be shown in [52,53].…”

Section: Structural Damage Localization Frameworkmentioning

confidence: 99%

“…The second subprocess is to perform the minimization task applied on the objective function. The detailed overall framework can be well observed in [52].…”

Section: Structural Damage Localization Frameworkmentioning

confidence: 99%

Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

2018

Self Cite

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Structural damage detection is a well-known engineering inverse problem in which the extracting of damage information from the dynamic responses of the structure is considered a complex problem. Within that area, the damage tracking in 3D structures is evaluated as a more complex and difficult task. Swarm intelligence and evolutionary algorithms (EAs) can be well adapted for solving the problem. For this purpose, a hybrid elitist-guided search combining a multiobjective particle swarm optimization (MOPSO), Lévy flights (LFs), and the technique for the order of preference by similarity to ideal solution (TOPSIS) is evolved in this work. Modal characteristics are employed to develop the objective function by considering two subobjectives, namely, modal strain energy (MSTE) and mode shape (MS) subobjectives. The proposed framework is tested using a well-known benchmark model. The overall strong performance of the suggested method is maintained even under noisy conditions and in the case of incomplete mode shapes.

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“…From the literature, vibration-based crack identification methods can be broadly classified as model-based Based on the above short review, there is a trend to transform the crack identification problem into an optimization problem, and through updating iterations to find the crack parameters minimizing the difference between measured features and calculated features, which can be classified as model updating method [36]. The model updating method has been widely used in structural damage identification [37][38][39][40], and what matters the most are the model construction and identification efficiency. For the static beam or plate, an accurate model is far more easily to obtain, which will be not the case for rotating rotors, especially when a breathing crack is there.…”

Section: Introductionmentioning

confidence: 99%

A Super-Harmonic Feature Based Updating Method for Crack Identification in Rotors Using a Kriging Surrogate Model

Ouyang

2019

Applied Sciences

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Dynamic model updating based on finite element method (FEM) has been widely investigated for structural damage identification, especially for static structures. Despite the substantial advances in this method, the key issue still needs to be addressed to boost its efficiency in practical applications. This paper introduces the updating idea into crack identification for rotating rotors, which has been rarely addressed in the literature. To address the problem, a novel Kriging surrogate model-based FEM updating method is proposed for the breathing crack identification of rotors by using the super-harmonic nonlinear characteristics. In this method, the breathing crack induced nonlinear characteristics from two locations of the rotors are harnessed instead of the traditional linear damage features for more sensitive and accurate breathing crack identification. Moreover, a FEM of a two-disc rotor-bearing system with a response-dependent breathing crack is established, which is partly validated by experiments. In addition, the associated breathing crack induced nonlinear characteristics are investigated and used to construct the objective function of Kriging surrogate model. Finally, the feasibility and the effectiveness of the proposed method are verified by numerical experiments with Gaussian white noise contamination. Results demonstrate that the proposed method is effective, accurate, and robust for breathing crack identification in rotors and is promising for practical engineering applications.

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Structural damage detection using finite element model updating with evolutionary algorithms: a survey

Cited by 183 publications

References 140 publications

The synthesis of data from instrumented structures and physics-based models via Gaussian processes

The synthesis of data from instrumented structures and physics-based models via Gaussian processes

Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

A Super-Harmonic Feature Based Updating Method for Crack Identification in Rotors Using a Kriging Surrogate Model

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