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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. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) have been used in various monitoring applications for the past two decades, as one of the very few options to provide absolute positions in a global reference frame. However, high performance GNSS stations are expensive, and sometimes may be impractical because of their size, power consumption or software requirements. Thus, the use of low-cost GNSS stations for structural health monitoring (SHM) has gained increasing attention. This paper presents a detailed experimental assessment of multiple combinations of GNSS receivers and antennas, and highlights an optimal cost-efficient solution for monitoring applications. Several sets of processing parameters and constraints are also evaluated using open source RTKLib software. The performance of the proposed solution is evaluated through two experimental dynamic scenarios, proving its ability to track quick displacements down to 4 mm and oscillations of 1 cm with a frequency up to 0.25 Hz with a 1 Hz receiver. Finally, a two-week dataset acquired from on a network of low-cost GNSS stations deployed on a suspended bridge is used to validate on-site performance. Results show good agreement between GNSS time series, traditional displacement sensors, and numerical simulations made using an operational mechanical model of the bridge, highlighting the potential of such low-cost solutions for structural health monitoring applications. [ABSTRACT FROM AUTHOR]