Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation

Zhu, Xiang; Cao, Maosen; Ostachowicz, Wiesław; Xu, Weilai

doi:10.3390/s19030463

Cited by 33 publications

(19 citation statements)

References 123 publications

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“…The procedure of this technique contains two steps: (i) to decompose the original signal by the empirical mode decomposition (EMD) method into a series of complete and oscillatory components, named intrinsic mode functions (IMFs), and (ii) to capture the instantaneous frequency and amplitude features by applying Hilbert transform (HT) to the IMFs. The vast majority of studies in the literature utilized HHT based on EMD for SHM and damage detection purposes [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Mousavi

Zhang

Masri

et al. 2020

Sensors

117

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Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.

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Section: Introductionmentioning

confidence: 99%

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Mousavi

Zhang

Masri

et al. 2020

Sensors

117

View full text Add to dashboard Cite

show abstract

“…2 Mainly, the extraction of modal parameters is the basis of the judgment relating to structural integrity and safety. [3][4][5] It should be mentioned that these types of SHM methods are less applicable for structures such as tunnels compared to bridges and buildings. In practice, health monitoring using vibrational responses usually is achieved by measuring changes in modal parameters such as natural frequencies that require a high level of damage state.…”

Section: Introductionmentioning

confidence: 99%

“…Investigation of the vibrational response is a typical method which is utilized for structural health monitoring (SHM) over the past decades 2 . Mainly, the extraction of modal parameters is the basis of the judgment relating to structural integrity and safety 3–5 . It should be mentioned that these types of SHM methods are less applicable for structures such as tunnels compared to bridges and buildings.…”

Section: Introductionmentioning

confidence: 99%

Arc Length method for extracting crack pattern characteristics

Asjodi

Daeizadeh

Hamidia

et al. 2020

Struct Control Health Monit

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Although manual crack inspection has been widely used for structural health monitoring over the last decades, the development of computer vision methods allows continuous monitoring and compensates the human judgment inaccuracy. In this study, an image-based method entitled Arc Length method is introduced for extracting crack pattern characteristics, including crack width and crack length. The method contains two major steps; in the first step, the crack zones are estimated in the whole image. Afterwards, the algorithm finds the start point, follows the crack pattern, and measures the crack features, such as crack width, crack length, and crack pattern angle. The efficiency of the method is validated using a few case studies from cracked structural concrete shear walls tested in the laboratory under quasi-static cyclic loadings. The case studies show high efficiency of the proposed method in following the crack patterns even when the crack propagates in two or more branches. The application of this approach plays a significant role in crack monitoring of infrastructures, such as concrete bridges and tunnels.

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“…Moreover, response variations may be masked by the uncertainties in the structural parameters of practical structures, as well as by the presence of noise. All of these factors result in the raw data being uninformative regarding the occurrence of structural changes, therefore, resulting in the need for feature extraction of measurement data [3]. In order to detect damage effectively, the extracted features are required to be sensitive to damage, while insensitive to parametric uncertainties or noise.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis Method with Space and Time Windows for Damage Detection

Zhang

Tang

Zhou

et al. 2019

Sensors

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Long-term structural health monitoring (SHM) has become an important tool to ensure the safety of infrastructures. However, determining methods to extract valuable information from large amounts of data from SHM systems for effective identification of damage still remains a major challenge. This paper provides a novel effective method for structural damage detection by introduction of space and time windows in the traditional principal component analysis (PCA) technique. Numerical results with a planar beam model demonstrate that, due to the presence of space and time windows, the proposed double-window PCA method (DWPCA) has a higher sensitivity for damage identification than the previous method moving PCA (MPCA), which combines only time windows with PCA. Further studies indicate that the developed approach, as compared to the MPCA method, has a higher resolution in localizing damage by space windows and also in quantitative evaluation of damage severity. Finally, a finite-element model of a practical bridge is used to prove that the proposed DWPCA method has greater sensitivity for damage detection than traditional methods and potential for applications in practical engineering.

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Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation

Cited by 33 publications

References 123 publications

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Arc Length method for extracting crack pattern characteristics

Principal Component Analysis Method with Space and Time Windows for Damage Detection

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