Cited by 55Structural Health Monitoring TechniquesStructural Engineering and Materials AnalysisInfrastructure Maintenance and Monitoring

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Correlation model of deflection, vehicle load, and temperature for in-service bridge using deep learning and structural health monitoring

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Deng, Yang, Ju, Hanwen, Zhai, Wenqiang, Li, Aiqun, Ding, Youliang·2022·Structural Control and Health Monitoring

DOI 10.1002/stc.3113

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Abstract

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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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
ORCID: Deng, Yang
Ju, Hanwen
Li, Aiqun
Ding, Youliang
Venue: Structural Control and Health Monitoring
Publisher: Wiley
Published: 2022-10-07
Volume: 29
Issue: 12
ISSN: 1545-2255 (ISSN-L)
1545-2263
DOI: 10.1002/stc.3113
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: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
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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Correlation model of deflection, vehicle load, and temperature for in-service bridge using deep learning and structural health monitoring

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