Combining GNSS and accelerometer measurements for evaluation of dynamic and semi-static characteristics of bridge structures

Abstract

With the increasing number of long span bridges, real-time, accurate and continuous monitoring of their safety is important at present. This study investigates the combination of a global navigation satellite system (GNSS) and accelerometer for monitoring dynamic and semi-static characteristics of bridge structures. A field experiment was conducted with the integration of a GNSS and accelerometer. Considering the noise interference of GNSS monitoring, performance tests were first conducted in different environments to investigate the noise characteristics. Next, complete ensemble empirical mode decomposition with adaptive noise-wavelet packet (CEEMDAN-WP) algorithm was chosen for denoising, among which a double criterion based on the correlation coefficient and effective coefficient was proposed to sift the intrinsic mode functions. After the noise reduction process, structural dynamic displacements and modal frequencies were successfully extracted from the 50 Hz GNSS real-time kinematic (GNSS-RTK) and accelerometer data, in which the displacements presented a consistent trend and the first natural frequency was the same (i.e. 0.369 Hz). Structural semi-static characteristics were evaluated by using 1 Hz (RTK), post-processed kinematic, and precise point positioning data. With reference to relevant specifications, the structural failure probability of the bridge in the vertical direction was calculated to be 0.4319. The results indicate that GNSS-RTK is reliable in monitoring structural dynamic and semi-static displacements of the bridge. Additionally, the proposed improved CEEMDAN-WP with double criterion is effective for background noise reduction. In addition, there may be some non-adequate behaviors, such as heavy traffic and vehicle overload, leading to the critical operation of the bridge.

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

“…Finally, the effective IMFs are extracted through judgment criteria. Traditionally, the judgment and reconstruction of effective IMFs are based on correlation coefficients [6]. On the basis of Liu et al [32], this paper proposes a criterion based on the Pearson correlation coefficient between each decomposed IMF component Traditionally, the judgment and reconstruction of effective IMFs are based on correlation coefficients [6].…”

“…Traditionally, the judgment and reconstruction of effective IMFs are based on correlation coefficients [6]. On the basis of Liu et al [32], this paper proposes a criterion based on the Pearson correlation coefficient between each decomposed IMF component Traditionally, the judgment and reconstruction of effective IMFs are based on correlation coefficients [6]. On the basis of Liu et al [32], this paper proposes a criterion based on the Pearson correlation coefficient between each decomposed IMF component I MF i (n) and the original vibration data ω n for determining the effective IMFs, which is defined as:…”

“…Thirdly, the remaining IMF components are regarded as information components to be further selected. The components with a value of a Pearson correlation coefficient of less than 0.1 are classified as pseudo components, otherwise they are classified as meaningful components [6].…”

“…It has been widely used in the bridge deformation monitoring in the last few years [1][2][3][4]. However, due to the existence of the multipath effect and random errors, true dynamic displacements are often overflooded by strong noise, which limits the GNSS vibration monitoring in modal parameters identification [5,6]. Hence, data processing methods should be used to eliminate GNSS measurement error before extracting the structural dynamic characteristics.…”

A combined algorithm for denoising GNSS-RTK positioning solutions with application to displacement monitoring of a super-high-rise building

“…However, the correlation coefficient was more effective for identifying the low-frequency noise components, while the white noise dominated components cannot be effectively identified. To this end, the effective coefficient was introduced for further judgment of the components dominated by high-frequency white noise [17]. The definition of the effective coefficient is investigated in this paper.…”