An EEMD‐Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

Li, Bo; Zhang, Lixin; Zhang, Qiling; Yang, Sen

doi:10.1155/2019/4937595

Cited by 17 publications

(11 citation statements)

References 26 publications

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“…Among them, all the cracks originate from the deformation of the slope and extend to the interior of the embankment. e lateral displacements and length of the cracks are also positively correlated [57][58][59].…”

Section: Calculation Results Of the Response Surface Methodmentioning

confidence: 99%

Embankment Seismic Fragility Assessment under the Near‐Fault Pulse‐like Ground Motions by Applying the Response Surface Method

Che

Yin

Zhou

et al. 2021

Shock and Vibration

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Uncertainties of the ground motions and structural parameters are the main factors that limit the accuracy of embankment seismic fragility assessment. In response to the uncertainties of the ground motions, artificial synthesizing method of the near-fault pulse-like ground motions was proposed, and 15 ground motions with the rupture fault distances ranging from 1 to 15 km were synthesized by taking the Chi-Chi earthquake in Taiwan, China, as an example. The Xi’an-Baoji expressway K1125 + 470 embankment was taken as the research object, and a total of 12 structural parameters were selected as the design variables, namely, the elastic modulus, bulk modulus, shear modulus, density, cohesion force, and internal friction angle of the embankment fill and soil foundation, respectively. In response to the uncertainties of these parameters, 3 principal components with large impacts on the embankment seismic fragility were extracted based on the principal component analysis. Mapping relationships among the principal components and embankment seismic damages were analyzed using the uniform design response surface method, and the seismic fragility assessment was carried out and the fragility curves were plotted. The research results are consistent with the actual embankment seismic damage conditions of the Chi-Chi earthquake, indicating that the proposed method is scientific and reasonable. It also shows that it would obviously overestimate the seismic performance in the embankment seismic fragility assessment without considering the uncertainties of the ground motions and structural parameters.

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Section: Calculation Results Of the Response Surface Methodmentioning

confidence: 99%

Embankment Seismic Fragility Assessment under the Near‐Fault Pulse‐like Ground Motions by Applying the Response Surface Method

Che

Yin

Zhou

et al. 2021

Shock and Vibration

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show abstract

“…Lu [207] proposed the EMD-WP method that integrates EMD and wavelet packet to denoise the GNSS structure monitoring data, and it can also weaken the multipath effect. Li et al [208] proposed an adaptive filtering method, EEMD-Wavelet-SSA, which combined EEMD, wavelet threshold and SSA. This can significantly reduce the root mean square error under the condition of low SNR of GNSS monitoring data.…”

Section: Deformation Measurement Technology Based On Gnss For Bridge Structural Health Monitoringmentioning

confidence: 99%

Review of Bridge Structural Health Monitoring Based on GNSS: From Displacement Monitoring to Dynamic Characteristic Identification

Wang

Zhao

et al. 2021

IEEE Access

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Deformation monitoring and dynamic characteristic analysis of bridge structures are the vital and basic requirements for the safe operation of bridges. In recent years, Global Navigation Satellite System (GNSS) has become increasingly widely used in bridge structural health monitoring with the development of the GNSS technology, especially the continuous improvement and development of China's Beidou navigation satellite system (BDS). This article summarises the application process of GNSS dynamic deformation monitoring and the development of GNSS deformation measurement technology of bridge structural health monitoring, the dynamic characteristic identification method and its application in bridge GNSS monitoring. The positioning solution methods for GNSS monitoring, the high sampling rate GNSS receiver for monitoring, multi-frequency and multi-system GNSS monitoring and the weakening of multipath effect of GNSS monitoring are summarised in detail. Then, the conclusions and prospects are posed for future research and related application.

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“…The EEMD is an improved EMD model [33]. Based on EMD, a set of Gaussian white noise with normal distribution is added, which effectively improves the aliasing and discontinuity of signals at different scales and avoids the mode aliasing caused by EMD decomposition process [34][35][36]. The flowchart of EEMD is shown in Figure 7, illustrated as follows:…”

Section: Preprocessing Of Wind Speed Datamentioning

confidence: 99%

“…The initial parameters in this experiment were chosen from References [32,35]. Note that 10 tests were carried out to determine the optimal parameter values in FF and FPA models, concluded in Table 6.…”

Section: Wind Speed Forecasting Employed Eemd-ifpa-ff Modelmentioning

confidence: 99%

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Ren

Lin

2019

Energies

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It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

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An EEMD‐Based Denoising Method for Seismic Signal of High Arch Dam Combining Wavelet with Singular Spectrum Analysis

Cited by 17 publications

References 26 publications

Embankment Seismic Fragility Assessment under the Near‐Fault Pulse‐like Ground Motions by Applying the Response Surface Method

Embankment Seismic Fragility Assessment under the Near‐Fault Pulse‐like Ground Motions by Applying the Response Surface Method

Review of Bridge Structural Health Monitoring Based on GNSS: From Displacement Monitoring to Dynamic Characteristic Identification

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

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