Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach
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Bibliographic Details
| Author(s): |
Huaqiang Zhong
(State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China)
Limin Sun 
(State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China)
José Turmo
(Universitat Politècnica de Catalunya BarcelonaTECH, Barcelona, Spain)
Ye Xia (Department of Bridge Engineering, Tongji University, Shanghai, China) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Congress: Structural Engineering for Future Societal Needs, Ghent, Belgium, 22-24 September 2021 | ||||
| Published in: | IABSE Congress Ghent 2021 | ||||
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| Page(s): | 555-560 | ||||
| Total no. of pages: | 6 | ||||
| DOI: | 10.2749/ghent.2021.0555 | ||||
| Abstract: |
In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination. |
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| Keywords: |
frequency distribution Bridge structural health monitoring one-dimensional convolutional neural network data quality assessment
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| Copyright: | © 2021 International Association for Bridge and Structural Engineering (IABSE) | ||||
| License: | This creative work is copyrighted material and may not be used without explicit approval by the author and/or copyright owner. |
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