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Correlation model of deflection, vehicle load, and temperature for in-service bridge using deep learning and structural health monitoring
Open accessAbstract
Deflection is an important issue in bridge structural health monitoring. An accurate deflection–vehicle load–temperature correlation model is critical to abnormal data identification, deflection prediction under extreme conditions, and bridge structural assessment. However, because of the discrete distribution in time domain of vehicle load and the extreme complexity of the deflection–vehicle load–temperature correlation, the correlation modeling method needs further studies. A novel deflection–vehicle load–temperature correlation modeling method is developed in this study. Based on the concept of deflection influence line (DIL), the raw vehicle load monitoring data are transformed into time-continuous vehicle influence coefficient (VIC). By using gated recurrent unit (GRU) neural network, a correlation model with inputs of VIC and environmental temperature data and output of deflection data is established. Taking a suspension bridge in China as an example, the prediction accuracy of short-, medium-, and long-term correlation models is tested. Moreover, based on the correlation model, a decomposition method of temperature- and vehicle-induced deflection components is proposed. The results show that the predicted deflection of the short-term correlation model is basically consistent with the real-time monitoring data, while the medium- and long-term correlation models have accurate prediction ability for the deflection extreme values in a certain time window. The temperature- and vehicle-induced deflection components separated by using the correlation model are in good agreement with the wavelet decomposition (WD) results, with clear physical meaning and independent of empirical judgment.
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“Deng et al. [187] used the GRU to establish a model of vehicle influence coefficient, temperature input, and deflection output, aiming to accurately predict the suspension bridge’s deflection.”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [187]
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“Generally, the training set’s ratio to the test set is 8:2 [27,36,182,198] or 7:3 [15,58,187].”
From Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends (2023) · cites this paper as [187]
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Metadata
- Authors
- Deng, Y., Ju, H., Zhai, W., Li, A., & Ding, Y.
- Affiliations
- Beijing University of Civil Engineering and Architecture (CN)
- Southeast University (CN)
- Corresponding author
- Deng, Yang
- Venue
- Structural Control and Health Monitoring
- Publisher
- Wiley
- Published
- 2022-10-07
- Volume
- 29
- Issue
- 12
- ISSN
- 1545-2255 (ISSN-L)
- 1545-2263
- OpenAlex
- W4303419067
- Cited by
- 55
- References
- 48
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 21 · 2023: 5 · 2022: 1
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Structural Control and Health Monitoring (publishedVersion)
- Funders
- Beijing Municipal Commission of Education (CIT&TCD201904060)
- National Natural Science Foundation of China (51878027)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- vehicle loading, temperature effects, gated recurrent unit, existing bridges, suspension bridges, wavelet decomposition, abnormal data identification
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