Dynamic Parameter Identification of a Long-Span Arch Bridge Based on GNSS-RTK Combined with CEEMDAN-WP Analysis
Abstract
:1. Introduction
2. Stability Test of GNSS-RTK Receivers
3. The Principle of CEEMDAN-WP and RDT
3.1. EEMD, CEEMD, and CEEMDAN Algorithms
- Add Gauss white noise into the original signal and thus produce a new signal .
- Using the EMD algorithm, is decomposed into a number of IMF components and a residual component .
- By repeating the above two steps, the IMFs are obtained, where is the iteration number and is the mode.
- Compute the average of the IMF components to eliminate the effects of additional white noise.
- Add positive and negative white noise into the original signal , and generate two new signals .
- Repeat the above step, and decompose the new signals using EMD.
- Derive two sets of IMF components for the new signals.
- Calculate decomposition results by averaging multiple components.
- Define as the operator which produces the -th IMF which has been decomposed based on the EMD algorithm. The first mode is derived by EMD from the signal .
- Calculate the first residual signal.
- Decompose the signal to derive the first mode, after which the second mode is defined.
- For , calculate the -th residual signal.
- For , decompose the signal to derive the first mode, after which the -th mode is defined.
- Turn to step 4 for the next .
3.2. WP Method
3.3. The CEEMDAN-WP Model
- CEEMDAN is employed for decomposition to obtain a series of IMFs.
- Because of the existence of background noise, some IMF components are noise dominated. Reconstruct signals after removing the components which are noise dominated.
- A three-level WP is used to decompose the signal obtained in Step 2.
- Determine the classic wavelet basis.
- Select proper thresholds and quantify the decomposed coefficients.
- Reconstruct the signal and export.
3.4. The RDT Method
4. Performance Evaluation of the CEEMDAN-WP
5. Structural Dynamic Deformation Monitoring of Rainbow Bridge
5.1. Bridge Description and Test Plan
5.2. FEM of the Bridge
5.3. Vibration Signal Analysis
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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CEEMDAN | WP | CEEMDAN-WP | |
---|---|---|---|
SNR | 9.7908 | 9.3499 | 10.2086 |
RMSE (cm) | 0.3187 | 0.3258 | 0.3121 |
Steel Tube and Wind Brace | Concrete Inside the Steel Tube | Crossbeam and Stringer | Tie Bar | Bridge Deck | Pier Column | |
---|---|---|---|---|---|---|
Elasticity modulus (Pa) | 2.1 × 1011 | 3.5 × 1010 | 3.0 × 1010 | 2.1 × 1011 | 2.85 × 1010 | 3.3 × 1010 |
Density (kg/m3) | 7800 | 2600 | 2600 | 7800 | 2500 | 2600 |
Poisson coefficient | 0.3 | 0.1667 | 0.1667 | 0.3 | 0.1667 | 0.1667 |
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Xiong, C.; Yu, L.; Niu, Y. Dynamic Parameter Identification of a Long-Span Arch Bridge Based on GNSS-RTK Combined with CEEMDAN-WP Analysis. Appl. Sci. 2019, 9, 1301. https://doi.org/10.3390/app9071301
Xiong C, Yu L, Niu Y. Dynamic Parameter Identification of a Long-Span Arch Bridge Based on GNSS-RTK Combined with CEEMDAN-WP Analysis. Applied Sciences. 2019; 9(7):1301. https://doi.org/10.3390/app9071301
Chicago/Turabian StyleXiong, Chunbao, Lina Yu, and Yanbo Niu. 2019. "Dynamic Parameter Identification of a Long-Span Arch Bridge Based on GNSS-RTK Combined with CEEMDAN-WP Analysis" Applied Sciences 9, no. 7: 1301. https://doi.org/10.3390/app9071301