On acoustic emission for damage detection and failure prediction in fiber reinforced polymer rods using pattern recognition analysis

Abstract

Fiber reinforced polymer (FRP) rods are used for pre-stressing and reinforcing in civil engineering applications. Damage in FRP rods can lead to sudden brittle failure, therefore, a reliable method that provides indicators of damage progression and potential failure in FRP rods is highly desirable. Acoustic emission (AE) signal analysis has been used for damage detection and monitoring of FRP materials. In this study, a new AE event detection algorithm, utilizing the root mean square envelope of AE signal, is applied to AE data to isolate each AE event separately, even when AE events are nearly coincident. A fuzzy c-means (FCM) clustering algorithm is used to classify these isolated AE events into 3 clusters. Scanning electron microscopy images of FRP rod cross-sections also show 3 types of damage. The hypothesis in this study is that each cluster represents a damage mechanism. The number of events in each cluster is monitored versus the percent of the ultimate load. The ratio of the number of AE events in one of the FCM clusters to the number of AE events in another FCM cluster was useful for providing an indication of when the stress levels have reached the point where the loads may cause the FRP rod to fail. The results of applying this parameter to four FRP rods show a significant slope change (factor of 10) in this ratio at around 40% and 60% of the ultimate load for glass FRP rods and carbon FRP rods, respectively. This method may prove useful in damage progression and failure prediction of the FRP rods in prefabricated structures where pre-stressed FRP is used and in field monitoring of FRP materials.