Research and application of pattern recognition LSTM based bridge data anomaly detection

Gao, Zheng; Li, Funian; Yu, Xingsheng; Yan, Junfeng; Chen, Zhidan

doi:10.1109/cac57257.2022.10054767

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Farrar et al [232], in 1994, defined PR as a method to highlight the changes in the frequency response functions measured using cracked (damaged) and uncracked (undamaged) bridges. PR in BSHM involves applying methods such as statistical (SPR) [250], machine learning, time-frequency analysis, and data mining [251,252]. Differentiating between patterned or structural changes in the intact and damaged state under operational and environmental variations is the primary objective of the SPR paradigm.…”

Section: Pattern Recognition (Pr)mentioning

confidence: 99%

Operational Modal Analysis on Bridges: A Comprehensive Review

Hasani,

Freddi

2023

Infrastructures

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Structural health monitoring systems have been employed throughout history to assess the structural responses of bridges to both natural and man-made hazards. Continuous monitoring of the integrity and analysis of the dynamic characteristics of bridges offers a solution to the limitations of visual inspection approaches and is of paramount importance for ensuring long-term safety. This review article provides a thorough, straightforward examination of the complete process for performing operational modal analysis on bridges, covering everything from data collection and preprocessing to the application of numerous modal identification techniques in both the time and frequency domains. It also incorporates advanced methods to address and overcome challenges encountered in previous approaches. The paper is distinguished by its thorough examination of various methodologies, highlighting their specific advantages and disadvantages, and providing concrete illustrations of their implementation in practical settings.

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