A method to maximise the information obtained from low signal-to-noise acceleration data by optimising SSI-COV input parameters

Abstract

Structural Health Monitoring (SHM) has mainly been undertaken on larger bridges and on a case-by-case basis. This is due to a range of factors, such as the high installation costs and the effort required to install and commission the monitoring systems. One way in which SHM systems can become more feasible for widespread adoption at a network level is to reduce the number and cost of sensors used. However, this comes with a trade-off as low-cost sensors will typically have a worse signal-to-noise ratio and the reduced number of sensors requires careful placement to maximise the amount of information acquired. One of the simplest/cheapest methods of bridge SHM is long-term tracking of the bridge frequency to identify a change in stiffness. Using data collected from five in-service bridges, this research shows that the user-defined SSI-COV input parameters can significantly impact the quality of extracted natural frequencies. Consequently, a novel method is developed to aid in choosing the inputs used in the SSI-COV method. The method developed also showed that the determined inputs resulted in the extraction of accurate natural frequencies with minimal apparent outliers on all tested bridges. The developed method allows the extraction of quality natural frequencies from low signal-to-noise acceleration data which are vital when undertaking frequency-based SHM.