Modal parameter identification of a curved cable-stayed model bridge based on EDA and DATA-SSI
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Bibliographic Details
| Author(s): |
Xiaohang Zhou
(Bridge Engineering Department, Southwest Jiaotong University, Chengdu, China)
Deshan Shan (Bridge Engineering Department, Southwest Jiaotong University, Chengdu, China) Qiao Li (Bridge Engineering Department, Southwest Jiaotong University, Chengdu, China) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Symposium: Engineering the Future, Vancouver, Canada, 21-23 September 2017 | ||||
| Published in: | IABSE Symposium Vancouver 2017 | ||||
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| Page(s): | 253-260 | ||||
| Total no. of pages: | 8 | ||||
| Year: | 2017 | ||||
| DOI: | 10.2749/vancouver.2017.0253 | ||||
| Abstract: |
For bridge health monitoring, the measured data may be unreliable due to various interferences. In order to get a reliable modal parameter identification result of a bridge, it is vital to have an inspection and a pre-processing on the bridge health monitoring data. Firstly, exploratory data analysis (EDA) was adopted to inspect the data quality, and the unreliable data measured from malfunctioning sensor was removed. Then, outlier analysis was performed to eliminate the abnormal data points from the data set. In the end, data driven stochastic subspace identification (DATA-SSI) combined with stabilization diagram was applied to identify the bridge modal parameters. A large scale curved cable-stayed model bridge was taken as an instance to verify the proposed method. The comparison of the modal parameter identification results of the original and the pre-processed data shows that the proposed method is effective, accurate and valuable. |
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| Keywords: |
curved cable-stayed bridge exploratory data analysis outlier analysis data driven stochastic subspace identification
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