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

Shabbir, Faisal; Khan, Muhammad Imran; Ahmad, Naveed; Tahir, Muhammad Fiaz; Ejaz, Naveed; Hussain, Jawad

doi:10.3390/app7121245

Cited by 21 publications

(11 citation statements)

References 48 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent decades, research on robust damage identification techniques for civil structures under varying temperature conditions has gained momentum [11][12][13][14][15][16]. More recently, numerous machine learning techniques, such as support vector machine (SVM) [17], genetic algorithm (GA) [18], particle swarm optimization (PSO) [19], cuckoo search (CS) [20], and artificial neural network (ANN) [21][22][23], have been explored to eliminate temperature effects on vibration-based damage identification.…”

Section: Damage Identification Under Varying Temperature Effectsmentioning

confidence: 99%

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Huang

Zhao

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

The effect of varying temperatures is one of the most important challenges of vibration-based damage identification due to its bigger effects on the structural response than the damage itself. This study presents a methodology incorporating the autoregressive (AR) time series model with two-step artificial neural networks (ANNs) to identify damage under temperature variations. AR coefficients, which are extracted by fitting the AR models to acceleration responses, are however sensitive to temperature changes, resulting in false diagnoses. Thus, two-step ANN models with the inputs of difference in AR coefficients are utilized to compensate the detrimental temperature variations. Finite element (FE) models of a steel-braced frame structure, simulating several damage scenarios with different damage locations and severities at fluctuating temperatures, are used to verify the effectiveness and reliability of this approach. Numerical results indicate that the proposed approach could successfully recognize, locate, and quantify damage by using output-only vibration and temperature data regardless of varying temperatures and noise perturbations.

show abstract

Section: Damage Identification Under Varying Temperature Effectsmentioning

confidence: 99%

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Huang

Zhao

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…To date, a number of vibration-based system identification and damage detection methods have been developed, and review articles with different emphases can be also found [2][3][4]. In general, structural parameters identification can be performed in three different paradigms: (i) time domain (e.g., [5][6][7][8][9][10]), (ii) frequency domain (e.g., [11][12][13][14]), and (iii) time-frequency domain (e.g., [15][16][17]).…”

Section: Literature Surveymentioning

confidence: 99%

Output-Only Parameters Identification of Earthquake-Excited Building Structures with Least Squares and Input Modification Process

Yang

Jiang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Damage detection and system identification with output-only information is an important but challenging task for ensuring the safety and functionality of civil structures during their service life. In this paper, a relatively simple and efficient iteration identification method consisting of the least squares estimation (LSE) technique and an input modification process is proposed for the simultaneous identification of structural parameters and the unknown ground motion. The spatial distribution characteristics of ground acceleration on earthquake-excited building structures are considered as additional information for parameters identification in each iterative step. First, the unknown input is estimated using the measured responses and the initial guesses of the structural parameters. The estimated input is then modified on the basis of the property of its spatial distribution. This modified input is further employed for providing the updated estimation of structural parameters. The iterative procedure would continue until the preset convergence criterion is satisfied. The accuracy of the proposed approach is numerically validated via a shear building model under the El Centro earthquake. The effects of signal noise, the number of sample points, and the initial guesses of structural parameters are discussed. The results show that the proposed approach can satisfactorily identify the structural parameters and unknown earthquakes.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 21 publications

References 48 publications

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Damage Identification of a Steel Frame Based on Integration of Time Series and Neural Network under Varying Temperatures

Output-Only Parameters Identification of Earthquake-Excited Building Structures with Least Squares and Input Modification Process

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

Contact Info

Product

Resources

About