Data analysis and dynamic characteristic investigation of large-scale civil structures monitored by RTK-GNSS based on a hybrid filtering algorithm

Xiong, Chunbao; Chen, Wen

doi:10.1007/s13349-022-00580-6

Cited by 10 publications

(4 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various types of sensor devices such as linear variable differential transformers, optical fiber sensors, smartphones, vision cameras, and radars are used in SHM to measure the displacement responses, but dynamic deformation monitoring widely adopts GNSS and accelerometers for SHM non-stop or with high periodicity but not requiring gluing/embedding sensors into the structure [5][6][7][8]. The RTK-GNSS can achieve a subcentimeter-level measurement accuracy, and it is often selected for structural displacement estimation of large-scale structures such as long-span bridges and high-rise buildings, which usually have at least centimeter-level displacements [9][10][11]. However, the accuracy of GNSS positioning is typically limited due to environmental perturbations such as occlusion, diffraction, and reflection.…”

Section: Introductionmentioning

confidence: 99%

Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring

Dai,

Li,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Large-scale engineering structures deform and vibrate under the influence of external forces. Obtaining displacement and vibration is crucial for structural health monitoring (SHM). Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are complementary and widely used in SHM. In this paper, we propose an SHM scheme where IMU and multi-antenna GNSS are tightly integrated. The phase centers of multiple GNSS antennas are transformed into the IMU center, which increases the observation redundancy and strengthens the positioning model. To evaluate the performance of tight integration of IMU and multiple GNSS antennas, high-rate vibrational signals are simulated using a shaking table, and the errors of horizontal displacement of different positioning schemes are analyzed using recordings of a high-precision ranging laser as the reference. The results demonstrate that applying triple-antenna GNSS/IMU integration for measuring the displacement can achieve an accuracy of 2.6 mm, which is about 33.0% and 30.3% superior than the accuracy achieved by the conventional single-antenna GNSS-only and GNSS/IMU solutions, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring

Dai,

Li,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…How to find an effective algorithm to better evaluate the modal parameters of the bridge from the noisy signal responses has consistently attracted the interest of researchers. Some time-frequency domain analysis methods are employed to decompose signal responses into several intrinsic mode functions (IMFs) and thus separate noise components, like empirical wavelet transform (EWT) [14,15], empirical mode decomposition (EMD) [16][17][18], ensemble empirical mode decomposition (EEMD) [19][20][21], complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [22][23][24], variational mode decomposition (VMD) [25][26][27][28]. Tao et al [29] proposed the EWT method to reduce the noise in the GNSS coordinate system and demonstrated that the signal after EWT denoising was close to the original signal.…”

Section: Introductionmentioning

confidence: 99%

Modal frequencies evaluation of a damaged bridge using RCVMD algorithm based on sensor dynamic responses

Xiong

Shang

Liu

et al. 2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

This paper aims to employ high-rate global navigation satellite system real-time kinematic (GNSS-RTK) and triaxial accelerometer sensors to evaluate the modal frequencies of a damaged long-span bridge based on the proposed modified variational mode decomposition (VMD) algorithm. Firstly, in the modified VMD, the relative root mean square error (RRMSE) was presented to optimize the decomposition parameter K, the correlation criterion was introduced to select the suitable intrinsic mode function (IMF) components. The above procedure is called the RCVMD algorithm. Subsequently, the performance comparison evaluation experiments proved that the RCVMD algorithm was more advantageous in the decomposition and reconstruction of signals, had a better noise reduction effect in the signal with different noise levels, and was suitable to process the actual measurement response. Finally, in the field experiment, the modal frequencies of the damaged bridge were correctly detected from the GNSS-RTK displacement and acceleration responses according to the RCVMD algorithm, and the accelerometer picked up the high-order structural frequencies, demonstrating the reliability of the proposed method. The frequency characteristics of the damaged bridge are different from the normal state. The first and third-order frequencies increase, which is especially obvious in the first-order frequency, yet the second-order frequency decreases. The measured results have practical applications and can provide data support for the maintenance of the damaged bridge.

show abstract

“…Guo et al, proposed a filtering method combining EEMD and wavelet analysis to separate multipath effects in GNSS data [47]. Xiong et al, used CEEMDAN combined with wavelet transform [48] or wavelet packet [30] to conduct noise reduction processing on GNSS-RTK monitoring data of bridges and high-rise buildings. Although the noise reduction method of empirical mode decomposition combined with Sensors 2023, 23, 4268 3 of 21 wavelet analysis solved the problem of detail information loss to a certain extent, the filtering effect of wavelet analysis method depended on the selection of wavelet basis function, decomposition layers, and threshold.…”

Section: Introductionmentioning

confidence: 99%

Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN–Adaptive Threshold Wavelet Method

Yang

Xiang

et al. 2023

Sensors

View full text Add to dashboard Cite

From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data processing method of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), combined with adaptive threshold wavelet de-noising is proposed. The adaptive threshold wavelet filtering method composed of the mean and variance of wavelet coefficients of each layer is used to de-noise the BDS displacement monitoring data. CEEMDAN was used to decompose the displacement response data of the bridge to obtain the intrinsic mode function (IMF). Correlation coefficients were used to distinguish the noisy component from the effective component, and the adaptive threshold wavelet de-noising occurred on the noisy component. Finally, all IMF were restructured. The simulation experiment and the BDS displacement monitoring data of Nanmao Bridge were verified. The results demonstrated that the proposed method could effectively suppress random noise and multipath noise, and effectively obtain the real response of bridge displacement.

show abstract

Data analysis and dynamic characteristic investigation of large-scale civil structures monitored by RTK-GNSS based on a hybrid filtering algorithm

Cited by 10 publications

References 46 publications

Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring

Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring

Modal frequencies evaluation of a damaged bridge using RCVMD algorithm based on sensor dynamic responses

Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN–Adaptive Threshold Wavelet Method

Contact Info

Product

Resources

About