Temperature Effect on Continuous Modal Parameter Identification of Cable Stayed Bridge

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.

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Continuous Modal Parameter Identification of a Cable-Stayed Bridge Based on Robustious Decomposition and Covariance-Driven Stochastic Subspace Identification

Iranian Journal of Science and Technology Transactions of Civil Engineering ◽

10.1007/s40996-016-0008-1 ◽

2016 ◽

Vol 40 (1) ◽

pp. 11-22 ◽

Cited By ~ 2

Author(s):

I. Khan ◽

D. Shan ◽

Q. Li

Keyword(s):

Parameter Identification ◽

Subspace Identification ◽

Modal Parameter ◽

Modal Parameter Identification ◽

Stochastic Subspace Identification ◽

Cable Stayed Bridge

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Data Interpretation and Continuous Modal Parameter Identification of Cable Stayed Bridge

The Open Civil Engineering Journal ◽

10.2174/1874149501509010577 ◽

2015 ◽

Vol 9 (1) ◽

pp. 577-591 ◽

Cited By ~ 3

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.

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

Archives of Civil Engineering ◽

10.1515/ace-2015-0011 ◽

2015 ◽

Vol 61 (2) ◽

pp. 3-22 ◽

Cited By ~ 2

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.

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