Abstract

The primary objective of Structural Health Monitoring (SHM) is to determine whether a structure is performing as expected or there is any anomaly in its behavior as compared to the normal condition. It is also useful in detecting the existence, location and severity of damage. Vibration based damage detection methods are very frequently used in SHM. But due to complicated features of real life structures, there are uncertainties involved in the key input parameters (e.g. measured frequencies and mode shape data) which affect the performance of these methods. If vibration based methods are incorporated with semi-analytical method such as statistical pattern recognition techniques, better accuracy can result in structural health assessment. This paper explores the statistical pattern recognition techniques for damage detection and/or degradation in structures. A case study, the Portage Creek Bridge in Victoria, British Columbia has been used. The following two approaches of the statistical pattern recognition techniques have been used: statistical pattern comparison, and statistical model development. After filtering and normalizing the data; obtained from the SHM system installed in the bridge damage sensitive features have been extracted by Auto Regressive (AR) modeling of time series data. Both idle and excited states of the bridge are considered in this case. From the statistical analysis of the strain and acceleration data, it has been found that while the bridge is in a good condition, there is a small, but steady deterioration in its performance. The study also demonstrates the feasibility of the statistical pattern recognition techniques in assessing the structural condition of a practical structure.