scholarly journals Data Interpretation and Continuous Modal Parameter Identification of Cable Stayed Bridge

2015 ◽  
Vol 9 (1) ◽  
pp. 577-591 ◽  
Author(s):  
Inamullah Khan ◽  
Deshan Shan ◽  
Qiao Li ◽  
Jie He ◽  
Fei Long Nan
Keyword(s):  
Parameter Identification ◽  
Real Life ◽  
Data Interpretation ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Exploratory Data

For reliable identification of modal parameters, it is important to distinguish between abnormal data due to defects, malfunctioning, and anomalies in the sensors, from that of precise data. In case of long-term continuous monitoring data, it is imperative to identify any defects in the raw data very quickly and accurately to ensure that the identification is trustworthy. Exploratory Data Analysis (EDA) is employed for the purpose of quickly visualizing any defects and anomalies in the sensor’s data. Outlier analysis is employed to make some data treatment followed by auto and cross correlation to further elucidate any defects and anomalies in the collected data. Finally, covariance driven stochastic subspace identification (CO-SSI) with some improvements is employed to carry out the continuous modal parameter identification. The Sutong Yangtze river bridge, a long span Y-shape pylon cable stayed bridge with a main span of 1088m was chosen as a case study and the above proposed methods were applied. The result showed that the suggested method is very effective and can provide better and more accurate real life results in the continuous health monitoring of bridges.

scholarly journals Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Mingjin Zhang ◽  
Xu Huang ◽  
Yongle Li ◽  
Hao Sun ◽  
Jingyu Zhang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Parameter Identification ◽  
Suspension Bridge ◽  
Continuous Wavelet ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Long Span Bridges ◽  
Cable Stayed Bridge ◽  
Long Span ◽  
Ambient Excitation

Accurate and timely identification of modal parameters of long-span bridges is important for bridge health monitoring and wind tunnel tests. Wavelet analysis is one of the most advantageous methods for identification because of its good localization characteristics in both time and frequency domain. In recent years, the wavelet method has been applied more frequently in parameter identification of linear and nonlinear systems. In this article, based on wavelet ridges and wavelet skeleton, the improved modal parameter identification method was studied. To find the appropriate time-frequency resolution, an optimal wavelet basis design principle based on minimum Shannon entropy was proposed. Aiming at endpoint effect in wavelet transform, a prediction continuation method based on support vector machine (SVM) was proposed, which can effectively suppress the endpoint effect of the extended samples. In view of the fact that the ridges of metric matrices obtained by the traditional crazy climber algorithm cannot fully reflect the distribution of ridges of modulus value matrices of wavelet coefficients, an improved high-precision crazy climber algorithm was put forward to accurately identify the position of the ridge of wavelet coefficients. Finally, taking a long-span cable-stayed bridge and a long-span suspension bridge as the engineering background, improved continuous wavelet transform (CWT) was applied to modal parameter identification of bridge under ambient excitation. The modal parameters such as modal frequency, damping ratio, and mode shape were obtained. Compared with the calculation value of the numerical simulation of long-span cable-stayed bridge and wind tunnel test of long-span suspension bridge, the reliability of CWT for modal parameter identification of long-span bridges under ambient excitation was verified.

scholarly journals Modal Parameter Identification Of Cable Stayed Bridge Based On Exploratory Data Analysis

2015 ◽  
Vol 61 (2) ◽  
pp. 3-22 ◽  
Author(s):  
I. Khan ◽  
D. Shan ◽  
Q. Li
Keyword(s):  
Data Analysis ◽  
Parameter Identification ◽  
Exploratory Data Analysis ◽  
Accurate Data ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Cable Stayed Bridge ◽  
Suggested Technique ◽  
Exploratory Data ◽  
Sutong Bridge

AbstractIn order to identify the modal parameters of civil structures it is vital to distinguish the defective data from that of appropriate and accurate data. The defects in data may be due to various reasons like defects in the data collection, malfunctioning of sensors, etc. For this purpose Exploratory Data Analysis (EDA) was engaged to envisage the distribution of sensor’s data and to detect the malfunctioning with in the sensors. Then outlier analysis was performed to remove those data points which may disrupt the accurate data analysis. Then Data Driven Stochastic Sub-space Identification (DATA-SSI) was engaged to perform the modal parameter identification. In the end to validate the accuracy of the proposed method stabilization diagrams were plotted. Sutong Bridge, one of the largest span cable stayed bridge was used as a case study and the suggested technique was employed. The results obtained after employing the above mentioned techniques are very valuable, accurate and effective.

scholarly journals Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification

2021 ◽  
Vol 11 (23) ◽  
pp. 11432
Author(s):  
Xiangying Guo ◽  
Changkun Li ◽  
Zhong Luo ◽  
Dongxing Cao
Keyword(s):  
Parameter Identification ◽  
Modal Analysis ◽  
Moving Average ◽  
Random Fluctuation ◽  
High Frequency Component ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification

A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.

Modal Parameter Identification of a Rigid Frame-Continuous Girders Bridge under Ambient Excitation by Fast Bayesian FFT Method

2013 ◽  
Vol 639-640 ◽  
pp. 985-991 ◽  
Author(s):  
Jian Ping Han ◽  
Pei Juan Zheng
Keyword(s):  
Parameter Identification ◽  
Model Updating ◽  
Element Model ◽  
Modal Identification ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stochastic Subspace Identification ◽  
Rigid Frame ◽  
Ambient Excitation

Bayesian theory is adopted in modal parameter identification, finite element model updating and residual capacity evaluation of the structures recently. Fast Bayesian FFT modal identification approach provides a rigorous way to obtain modal parameters and well-separated modes using the fast Fourier transform under ambient excitation. Moreover, it avoids choosing the modal order or removing false modes based on the stable diagram and has its obvious advantages. In this paper, modal parameters of a rigid frame-continuous girders bridge under ambient excitation are identified by this approach. Comparison with stochastic subspace identification (SSI) method indicates that Fast Bayesian FFT is a good approach to identify the modal parameters even for a large number of measurement channels.

A Modal Parameter Identification Method Based on Improved Covariance-Driven Stochastic Subspace Identification

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Chen Wang ◽  
Minghui Hu ◽  
Zhinong Jiang ◽  
Yanfei Zuo ◽  
Zhenqiao Zhu
Keyword(s):  
Parameter Identification ◽  
Gas Turbines ◽  
Excitation Frequency ◽  
Modal Parameters ◽  
Subspace Identification ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Mode Identification ◽  
Stochastic Subspace Identification ◽  
Identification Method

Abstract For the quantitative dynamic analysis of aero gas turbines, accurate modal parameters must be identified. However, the complicated structure of thin-walled casings may cause false mode identification and mode absences if conventional methods are used, which makes it more difficult to identify the modal parameters. A modal parameter identification method based on improved covariance-driven stochastic subspace identification (covariance-driven SSI) is proposed. The ability to reduce the number of mode absences and the solving stability are improved by a covariance matrix dimension control method. Meanwhile, the number of false mode identification is reduced via a false mode elimination method. In addition, the real mode complementation and the excitation frequency mode screening can be realized by a multispeed excitation method. The numerical results of a typical rotor model and measured data of an aero gas turbine validated the proposed method.

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