Data Interpretation and Continuous Modal Parameter Identification of Cable Stayed Bridge

Inamullah Khan*, Deshan Shan, Qiao Li, Jie He, Fei Long Nan
School of Civil Engineering, Bridge Engineering Department, Southwest Jiaotong University, 610031, P. R. China.

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© 2015 Khan et al;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Civil Engineering, Bridge Engineering Department, Southwest Jiaotong University, 610031, P. R. China; Tel: +86-18280158259; E-mail:


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.

Keywords: Cable stayed bridge, continuous modal parameter, covariance driven stochastic subspace identification, exploratory data analysis.