Monitoring-based evaluation of dynamic characteristics of a long span suspension bridge under typhoons

Guo, Jian; Hu, Cheng Jie; Zhu, Min; Ni, Yi

doi:10.1007/s13349-020-00458-5

Cited by 26 publications

(7 citation statements)

References 40 publications

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“…Therefore, in this paper, the AE signals of the bridges in operation under certain specific loading state were tested in this experiment. Based on the signal of hardware filtering technology and spatial identification technology, as well as the wavelet packet energy analysis (Guo et al 2020(Guo et al , 2021 and wavelet packet entropy analysis (Safty and El-Zonkoly 2008;Yin et al 2004) the characteristic frequency bands were extracted from simulated acoustic emission signal and noise signal. Finally, the signal was clustered by using Konhonen's self-organizing feature map and neural network (SOM neural network) (Kohonen 1998) to establish an acoustic emission detection and recognition algorithm, which provided new ideas and methods for solving the noise reduction problem of bridge acoustic emission damage signal.…”

Section: Introduction 33mentioning

confidence: 99%

Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

Liu

et al. 2023

J. Perform. Constr. Facil.

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Identification Noise signal is one of the challenging problems in the health monitoring of bridge structure using acoustic emission monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common method in identifying of the signals from the defect of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focus on the AE noise signal from a bridge in operation state and other specific loading state, which is diagnosed in the hardware filtering technology, spatial identification and SOM neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50 %, and can barely filter strong noise signal. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90 % and 100 % respectively. The trained network is used to test183 sample signals, the defect signal detection accuracy reaches 76 % and 78.8 %, therefore, the noise signal filtering effect is significantly improved.

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Section: Introduction 33mentioning

confidence: 99%

Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

Liu

et al. 2023

J. Perform. Constr. Facil.

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“…In recent years, the bridge condition assessment technology based on modal characteristics has become a research hotspot in the field of bridge detection and health monitoring (Cruz and Salgado, 2010; Guo et al, 2021; Moughty and Casas, 2017). However, the tested modal characteristics are always inevitably affected by environmental factors in practical tests.…”

Section: Introductionmentioning

confidence: 99%

“…As revealed in plenty of engineering tests, temperature has a significant impact on modal characteristics, and the temperature-induced frequency change may be more impactful than the frequency change caused by structural damage in some cases. As a consequence, the results of bridge state assessment results based on dynamic characteristics are rendered inaccurate or even ineffective (Guo et al, 2021; Talebinejad et al, 2011; Xia et al, 2012; Zhou and Yi 2014). Therefore, it is of great significance to analyze the influence mechanism of temperature on structural modal characteristics for the bridge reliability evaluation.…”

Section: Introductionmentioning

confidence: 99%

Effects of temperature on modal characteristics of non-uniform rigid-frame bridges

Tan

Kong

et al. 2023

Advances in Structural Engineering

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The effect of temperature on structural modal characteristics plays an important role in structural health monitoring and evaluation. Herein, the free vibration analysis of rigid-frame bridge is conducted by taking into account the effect of temperature. Firstly, the non-uniform rigid-frame bridge is divided into several members to develop the flexural and axial vibration equations for members under the effect of temperature in accordance with Hamilton’s principle. Secondly, each member is further divided into several segments, and the transfer relationship between segments is established to obtain the generalized dynamic stiffness matrix of the member. Then, the generalized dynamic stiffness matrix of each member is constructed by using the numerical assembly method similar to the finite element method so as to obtain the characteristic equation of rigid-frame bridge. Furthermore, the natural frequency and mode shape of the whole rigid-frame bridge are calculated by using the algorithm of Wittrick-Williams. Finally, the dynamic characteristic test of Yong-an bridge and the numerical calculation of the rigid-frame bridge are carried out to verify the generality and accuracy of the proposed method. The results indicate that the change of temperature will lead to the secondary internal force and the change of Young’s modulus, which together affect the modal characteristics of the structure, and the change of natural frequency caused by the change of Young’s modulus is greater than that caused by the secondary internal forces. In addition, there is a negative correlation between temperature and natural frequency of the rigid-frame bridge, and different temperature gradient modes also have certain influence on the mode shape of rigid-frame bridge.

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“…In addition, researchers typically combine stabilization diagrams to determine the true modal information of the structure when using SSI to identify modal parameters. For example, Guo et al [7] compared the modal parameter identification results of bridges identified by SSI stabilization diagram and peak picking method in typhoon weather, and the results showed that SSI performed better. Besides, aiming at the false mode problem faced by traditional SSI methods, Huang et al [8] reduced the number of false steady-state points in the SSI stabilization diagram by reconstructing the Hankel matrix, thus enhancing the robustness of the SSI algorithm.…”

Section: Introductionmentioning

confidence: 99%

Intelligent damage diagnosis method for offshore platforms based on enhanced stabilization diagrams and convolutional neural network

Leng,

Feng,

Sun

et al. 2023

Meas. Sci. Technol.

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Considering that it is difficult to evaluate the damage state of offshore platform structures under environmental excitation by stochastic subspace identification (SSI) stability diagrams alone, an intelligent damage diagnosis method based on enhanced stability diagrams and convolutional neural network (CNN) is proposed. The data-driven SSI algorithm and covariance-driven SSI algorithm are utilized to identify stability diagrams of monitoring data, and the stability diagrams of the two algorithms are superimposed together for image enhancement. Further, the enhanced stability diagrams are used as input samples for CNN training to distinguish the damage state of the structure. In the meanwhile, the whale optimization algorithm is employed to optimize the hyper parameters of CNN to ulteriorly improve the recognition performance. The final test accuracy of CNN is 97.20%, and is 13.09% higher than before hyper parameter optimization, which indicates that the damage diagnosis method based on enhanced stability diagrams and CNN is reasonable and effective, and is expected to be applied to real-time damage diagnosis of offshore platform structures.

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Monitoring-based evaluation of dynamic characteristics of a long span suspension bridge under typhoons

Cited by 26 publications

References 40 publications

Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

Effects of temperature on modal characteristics of non-uniform rigid-frame bridges

Intelligent damage diagnosis method for offshore platforms based on enhanced stabilization diagrams and convolutional neural network

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