Structural evaluation of dynamic and semi-static displacements of the Juarez Bridge using GPS technology

B., G. Esteban Vazquez; Gaxiola‐Camacho, J. Ramon; Bennett, R. A.; Guzman-Acevedo, G. Michel; Gaxiola-Camacho, Ivan E.

doi:10.1016/j.measurement.2017.06.026

Cited by 61 publications

(20 citation statements)

References 12 publications

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“…Moreover, the real-time kinematic global positioning system (RTK-GPS) device is employed to record the relative 3D positions of the rovers (i.e., the mobile stations) with respect to the base (i.e., the reference station) in the geographic coordinate system (X, Y, Z), as shown in Figure 3 [20][21][22]. e estimation accuracy of relative 3D positions can be improved by double-difference calculation of carrier phases from the base and rovers [20,22].…”

Section: Description Of Bridge Health Monitoringmentioning

confidence: 99%

Evaluation of the Wind‐Resistant Performance of Long‐Span Cable‐Stayed Bridge Using the Monitoring Correlation between the Static Cross Wind and Its Displacement Response

Gao-xin

Feng

2018

Shock and Vibration

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The wind-resistant performance of existing long-span cable-stayed bridge structures will inevitably deteriorate due to concrete carbonation, reinforcement corrosion, accumulated fatigue damage, etc., which can threaten the entire bridge safety. However, few studies currently focus on assessing the wind-resistant performance of existing long-span cable-stayed bridge structures, and at present, no studies have revealed how to identify the deterioration of the wind-resistant performance using a bridge health-monitoring technique. Therefore, based on the health-monitoring system installed on the Sutong Cable-stayed Bridge, the monitoring wind field and GPS displacement are captured to analyze the wind-resistant performance. First, the correlation between static cross wind and transversal displacement is analyzed, which is nearly linear but also contains discrete points caused by environmental noise. Second, considering that the discrete points can decrease the identification accuracy, one new method called the cross-correlation analysis of wavelet packet coefficients is put forward to effectively remove the discrete points. Third, considering that the traditional function cannot match the monitoring correlation very well, some new fitting functions are thoroughly studied to determine the best function for fitting the monitoring correlation. Fourth, the abnormal variation in the monitoring correlation caused by a deterioration in the wind-resistant performance is studied, and the root-mean-square (RMS) variable, which represents the difference between a good service state and a deteriorated service state, is used as a detection indicator to identify the deterioration of wind-resistant performance. Finally, the monitoring data from ten months are selected to evaluate the wind-resistant performance of the Sutong Bridge, and the result shows that its wind-resistant performance was still in a good service state during these ten months.

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Section: Description Of Bridge Health Monitoringmentioning

confidence: 99%

Evaluation of the Wind‐Resistant Performance of Long‐Span Cable‐Stayed Bridge Using the Monitoring Correlation between the Static Cross Wind and Its Displacement Response

Gao-xin

Feng

2018

Shock and Vibration

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“…Additionally, the GNSS positioning technology can overcome some shortcomings of traditional monitoring methods, as it easily identifies low-frequency structural vibration responses. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Fang

Meng

2021

Remote Sensing

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It is difficult to accurately identify the dynamic deformation of bridges from Global Navigation Satellite System (GNSS) due to the influence of the multipath effect and random errors, etc. To solve this problem, an improved empirical wavelet transform (EWT)-based procedure was proposed to denoise GNSS data and identify the modal parameters of bridge structures. Firstly, the Yule–Walker algorithm-based auto-power spectrum and Fourier spectrum were jointly adopted to segment the frequency bands of structural dynamic response data. Secondly, the improved EWT algorithm was used to decompose and reconstruct the dynamic response data according to a correlation coefficient-based criterion. Finally, Natural Excitation Technique (NExT) and Hilbert Transform (HT) were applied to identify the modal parameters of structures from the decomposed efficient components. Two groups of simulation data were used to validate the feasibility and reliability of the proposed method, which consisted of the vibration responses of a four-storey steel frame model, and the acceleration response data of a suspension bridge. Moreover, field experiments were carried out on the Wilford suspension bridge in Nottingham, UK, with GNSS and an accelerometer. The fundamental frequency (1.6707 Hz), the damping ratio (0.82%), as well as the maximum dynamic displacements (10.10 mm) of the Wilford suspension bridge were detected by using this proposed method from the GNSS measurements, which were consistent with the accelerometer results. In conclusion, the analysis revealed that the improved EWT-based method was capable of accurately identifying the low-order, closely spaced modal parameters of bridge structures under operational conditions.

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“…Many kinds of measurement technologies are applied in the deformation analysis. 24–26 Vazquez 27 investigate the dynamic and semi-static displacement of a bridge with the GPS time series. Kibitkin et al 28 analyzed the displacement vector fields and standard errors of deformation to choose the input parameters for the digital image correlation (DIC) method.…”

Section: Introductionmentioning

confidence: 99%

Network method for deformation analysis of three-dimensional point cloud with terrestrial laser scanning sensor

Yang

2018

International Journal of Distributed Sensor Networks

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The complexity of structural materials is increasing the importance of the technology for high accuracy measurement. How to obtain the displacement information of structural feature points accurately and efficiently is the key issue of deformation analysis. In this article, displacement analysis of a composite arched structure is investigated based on the terrestrial laser scanning technique. A new method based on the measured point cloud is proposed to analyze the displacement of surficial points, resulting in not only the displacement size but also the displacement direction. The innovation lies in extracting the displacement information with a network and remapped point cloud, which is called the network method. The displacement map obtained demonstrates that the transverse displacement in the experiment plays an important role in the safety of the structure, which could not be observed and obtained by the surface approximation method. Therefore, the panorama-and pointwise displacement analysis technologies contribute to ensure the safety of increasingly complex constructions.

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Structural evaluation of dynamic and semi-static displacements of the Juarez Bridge using GPS technology

Cited by 61 publications

References 12 publications

Evaluation of the Wind‐Resistant Performance of Long‐Span Cable‐Stayed Bridge Using the Monitoring Correlation between the Static Cross Wind and Its Displacement Response

Evaluation of the Wind‐Resistant Performance of Long‐Span Cable‐Stayed Bridge Using the Monitoring Correlation between the Static Cross Wind and Its Displacement Response

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

Network method for deformation analysis of three-dimensional point cloud with terrestrial laser scanning sensor

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