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Analyzing GNSS Measurements to Detect and Predict Bridge Movements Using the Kalman Filter (KF) and Neural Network (NN) Techniques

1
Geodesy and Earth Observation Group, Department of Planning, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark
2
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, North Kargar Street, Central Building of the College of Engineering, Tehran 1439957131, Iran
*
Author to whom correspondence should be addressed.
Received: 30 December 2020 / Revised: 28 January 2021 / Accepted: 1 February 2021 / Published: 7 February 2021
In this study, we present a data processing framework to apply measurements of the Global Navigation Satellite System (GNSS) technique for analyzing and predicting the movements of civil structures such as bridges. The proposed approach reduces the noise level of GNSS measurements using the Kalman Filter (KF) approach and enables the estimation of static, semi-static, and dynamic components of the bridge’s movements using a series of analyses such as the temporal filtering and the Least Squares Harmonic Estimation (LS-HE). The numerical results indicate that by using a RTK-GNSS system the semi-static component is extracted with a Standard Deviation (STD) of 0.032, 0.048, and 0.06 m in the North, East, and Up (NEU) directions, while that of the dynamic component is 0.004, 0.003, and 0.01 m, respectively. Comparing the dominant frequencies of the bridge movements from LS-HE with those of the permanent stations provides information about the bridge’s stability. To predict its deflection, the Neural Network (NN) technique is tested to simulate the time-varying components, which are then compared with the safety limits, known by its design, to assess the structural health under usual load. View Full-Text
Keywords: bridge movement modeling; Structural Health Monitoring; GNSS; Kalman Filter (KF); Least Squares Harmonic Estimation (LS-HE); Neural Network (NN) bridge movement modeling; Structural Health Monitoring; GNSS; Kalman Filter (KF); Least Squares Harmonic Estimation (LS-HE); Neural Network (NN)
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MDPI and ACS Style

Forootan, E.; Farzaneh, S.; Naderi, K.; Cederholm, J.P. Analyzing GNSS Measurements to Detect and Predict Bridge Movements Using the Kalman Filter (KF) and Neural Network (NN) Techniques. Geomatics 2021, 1, 65-80. https://0-doi-org.brum.beds.ac.uk/10.3390/geomatics1010006

AMA Style

Forootan E, Farzaneh S, Naderi K, Cederholm JP. Analyzing GNSS Measurements to Detect and Predict Bridge Movements Using the Kalman Filter (KF) and Neural Network (NN) Techniques. Geomatics. 2021; 1(1):65-80. https://0-doi-org.brum.beds.ac.uk/10.3390/geomatics1010006

Chicago/Turabian Style

Forootan, Ehsan; Farzaneh, Saeed; Naderi, Kowsar; Cederholm, Jens P. 2021. "Analyzing GNSS Measurements to Detect and Predict Bridge Movements Using the Kalman Filter (KF) and Neural Network (NN) Techniques" Geomatics 1, no. 1: 65-80. https://0-doi-org.brum.beds.ac.uk/10.3390/geomatics1010006

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