A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform

Amezquita-Sanchez, Juan P.; Park, Hyo Seon; Adeli, Hojjat

doi:10.1016/j.engstruct.2017.05.054

Cited by 143 publications

(107 citation statements)

References 38 publications

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“…Classical system‐identification methods have been reviewed in previous papers (Reyders, ; Sirca & Adeli, ). The literature is also replete with recent developments on system identification (Amezquita‐Sanchez, Park, & Adeli, ; Huang & Beck, ; Kim, Oh, Park, Shim, & Kim, ; Li, Park, & Adeli, ; Oh, Kim, & Park, ; Perez‐Ramirez et al., ; Yao et al., ; Yin, Yuen, Lam, & Zhu, ). Vibration‐based SDD methods with system identification have explored the correlations between the vibration features (natural frequency, mode shape, and their derivatives) and the structural damage information (Chang & Kim, ; Dutta & Talukdar, ; Ndambi, Vantomme, & Harri, ; Pandey et al., ; Ratcliffe, ; Reynders et al., ; Spiridonakos & Chatzi, ; Yan, Kerschen, De Boe, & Golinval, ).…”

Section: Introductionmentioning

confidence: 99%

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Zhang

Miyamori

Mikami

et al. 2019

Computer aided Civil Eng

219

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Deep learning has ushered in many breakthroughs in vision‐based detection via convolutional neural networks (CNNs), but the vibration‐based structural damage detection by CNN remains being refined. Thus, this study proposes a simple one‐dimensional CNN that detects tiny local structural stiffness and mass changes, and validates the proposed CNN on actual structures. Three independent acceleration databases are established based on a T‐shaped steel beam, a short steel girder bridge (in test field), and a long steel girder bridge (in service). The raw acceleration data are not pre‐processed and are directly used as the training and validation data. The well‐trained CNN almost perfectly identifies the locations of small local changes in the structural mass and stiffness, demonstrating the high sensitivity of the proposed simple CNN to tiny structural state changes in actual structures. The convolutional kernels and outputs of the convolutional and max pooling layers are visualized and discussed as well.

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Section: Introductionmentioning

confidence: 99%

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Zhang

Miyamori

Mikami

et al. 2019

Computer aided Civil Eng

219

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“…Hence, such methods may not provide necessary guidance for the maintenance of historic structures, which can result in a substantial increase in maintenance costs. One solution to these problems is to use sensor‐based SHM systems (Amezquita‐Sanchez and Adeli, ; Karami and Akbarabadi, ; Amezquita‐Sanchez et al., ; Liao, ). However, collecting data using various sensors and monitoring every historic structure in a short time require a large expense.…”

Section: Introductionmentioning

confidence: 99%

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

Wang

Zhao

et al. 2018

Computer aided Civil Eng

121

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Manual inspection (i.e., visual inspection and/or with professional equipment) is the most predominant approach for identifying and assessing superficial damage of masonry historic structures at present. However, this method is costly and at times difficult to apply to remote structures or components. Existing convolutional neural network (CNN)‐based damage detection methods have not been specifically designed for the multiple damage identification of masonry historic structures. To overcome these limits, a deep architecture of CNN damage classification techniques for masonry historic structures is proposed in this article using a sliding window‐based CNN method to identify and locate four categories of damage (intact, crack, efflorescence, and spall) with an accuracy of 94.3%. This is the first attempt to identify the multidamage of historic masonry structures based on CNN techniques and achieve excellent classification results. The data are only trained and tested from images of the Forbidden City Wall in China, and the pixel resolutions of stretcher brick images and header brick images are 480 × 105 and 210 × 105, respectively. Two CNNs (AlexNet and GoogLeNet) are both trained on a small dataset (2,000 images for training, 400 images for validation and testing) and a large dataset (20,000 images for training, 4,000 images for validation and testing). The performance of the trained model (94.3% accuracy) is examined on five new images with 1,860 × 1,260 pixel resolutions.

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“…The estimated curve can be written as follows:

G_{fitted} ((), t) = {\overset{truê}{A}}_{r} e^{- italicdt},

where

{\overset{truê}{A}}_{r}

represents the fitted amplitude of the r th free vibration response, d defines the decay rate of the exponential function, which can be estimated based on the nonlinear curve‐fitting analysis. According to the obtained damped free vibration response, the damping ratio can be approximately identified as follows:

{\overset{truê}{A}}_{r} e^{- italicdt} \approx A_{r} ((), t) e^{- ξ_{r} θ_{ri} ((), t)} \Rightarrow ξ_{r} = \frac{b}{θ_{r}} .

In addition, considering the r th modal contributions φ kr q r ( t ) and φ lr q r ( t ) from the k th and l th DOFs respectively, the mode shape can be identified as follows:

(||, \frac{φ_{kr}}{φ_{lr}}) = \frac{A_{kr} ((), t)}{A_{lr} ((), t)} .

…”

Section: Operational Modal Identification Based On the Improved Ewt Amentioning

confidence: 99%

Operational modal identification of structures based on improved empirical wavelet transform

Hao

2019

Struct Control Health Monit

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Summary This paper proposes an improved Empirical Wavelet Transform (EWT) approach for structural operational modal identification based on measured dynamic responses of structures under ambient vibrations. Two steps are involved in the improved EWT approach. In the first step, the standardized autoregressive power spectrum of the measured response is calculated to define the boundaries of frequency components for the subsequent EWT analysis. The second step is to decompose the measured response into a number of Intrinsic Mode Functions (IMFs) by using EWT. When the Intrinsic Mode Functions are obtained, structural modal information such as natural frequencies, mode shapes, and damping ratios can be identified by using Hilbert transform and Random Decrement Technique. In numerical studies, a simulated signal is used to investigate the effectiveness of the proposed approach. Operational modal identification based on the proposed approach and procedure is conducted to identify the modal parameters of a simulated spatial frame structure under the ambient excitations. The proposed approach is further used for operational modal identification of a seven‐storey shear type steel frame structure in the laboratory and a real footbridge under ambient vibrations to verify the accuracy and performance. The modal identification results from both numerical simulations and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.

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A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform

Cited by 143 publications

References 38 publications

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

Operational modal identification of structures based on improved empirical wavelet transform

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