Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection
Publication: Journal of Structural Engineering
Volume 148, Issue 11
Abstract
Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
The support from ANRT is gratefully acknowledged. The authors want to thank the Conseil départemental de Seine Maritime for granting access to the structure and monitoring data, and also Cerema for granting access to monitoring data. The opinions and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.
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Information & Authors
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Published In
Journal of Structural Engineering
Volume 148 • Issue 11 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 15, 2021
Accepted: May 23, 2022
Published online: Aug 26, 2022
Published in print: Nov 1, 2022
Discussion open until: Jan 26, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Bridge engineering
- Bridges
- Bridges (by type)
- Cable stayed bridges
- Cables
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Equipment and machinery
- Geomatics
- Global navigation satellite systems
- Mathematics
- Measurement (by type)
- Sensors and sensing
- Statistics
- Structural behavior
- Structural engineering
- Surveying methods
- Time series analysis
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