Signal selection and analysis methodology of long-term vibration data from the I-35W St. Anthony Falls Bridge

Large-scale, long-term structural health monitoring systems have become more feasible in recent years as the required data acquisition and analysis systems are more affordable to deploy. These long-term systems must process and store vast amounts of data without wasting computational power and storage capacity with redundant or poor quality data. While not a primary system for damage detection, large-scale, long-term vibration monitoring systems aim to leverage changes in the dynamic signature of a structure to assess global structural changes. Although the ability to continually collect vibration data at high rates exists, it is not always feasible to store all these data long term. As more long-term monitoring systems are deployed, efficient methods need to be developed to quickly and efficiently analyze large quantities of vibration data so that only the most pertinent information is archived. Previous researchers have used scheduled approaches, eg, taking data every hour, or triggered sensing systems. A monitoring system on the I-35W St. Anthony Falls Bridge, which crosses the Mississippi River in Minneapolis, Minnesota, has been collecting vibration and temperature data since the structure's opening in 2008. This provides a uniquely large data set to establish the characteristics of a good signal for output-only system identification to consistently and efficiently capture natural frequencies and mode shapes. To this end, a system identification routine using a novel signal selection approach and modal sorting routine that leverages NExT-ERA/DC is proposed to analyze this large data set. The resulting information allows long-term and temperature-based trends to be identified.