期刊
STRUCTURES
卷 41, 期 -, 页码 1546-1558出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.istruc.2022.05.071
关键词
High-speed railway bridge; Structural condition assessment; Influence line; Dynamic load factor; MFIR filtering; GMM clustering
资金
- National Key R&D Program of China [2021YFF0500900]
- Fund for Distinguished Young Scientists of Jiangsu Province [BK20190013]
- National Natural Science Foundation of China [51978154, 52008099]
- Natural Science Foundation of Jiangsu Province [BK20200369]
- Fundamental Research Funds for the Central Universities [2242022k30031, 2242022k3003]
The monitoring data of high-speed railway bridges can be used to assess their structural condition in real time. A data-driven approach using train-induced response features, extracted from the measured data, is developed for structural condition assessment. Filtering and clustering techniques are applied to achieve the evaluation and degradation warning of bridge structures.
The monitoring data makes it feasible to inspect and assess the structural condition of bridges in real time. Among the diverse in situ data of high-speed railway bridges under varying operational environments, dynamic responses caused by passing trains can offer insight into the mechanical properties of the bridge structure. Based on the train-induced response features of influence line (IL) and dynamic load factors (DLF) extracted from raw measured data, a comprehensive data-driven approach is developed for structural condition assessment of highspeed railway bridges, which is applied to a long-span steel-truss bridge as a validation. Considering the sparsity of the train-induced static response in the frequency domain, the multi-band finite impulse response (MFIR) filtering is used to extract the train-induced response features. The features are clustered via the Gaussian mixture model (GMM), and the two-level objective for structural condition evaluation and degradation warning of bridges can be achieved through the dynamically updated clustering results and probabilistic models. The results demonstrate that (1) MFIR filtering can effectively reject abnormal or interfering data and accurately extract the train-induced response features, and (2) the intrinsic nature and laws of features can be revealed by GMM clustering, which provides a statistical premise for the reliability analysis of bridge structures.
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