Performance analysis of simplified Fuzzy ARTMAP and Probabilistic Neural Networks for identifying structural damage growth

Abstract

Structural Health Monitoring (SHM) has been advancing worldwide as evidenced by recent papers and practical applications. SHM systems increase safety and reduce maintenance costs in a variety of applications contributing significantly to prevent structural failures. Studies focusing on the structural damage identification have been proposed in literature by using methods based on Probabilistic Neural Networks (PNN). Although Fuzzy based methods are recurrent in SHM approaches, the Simplified Fuzzy ARTMAP Network (SFAN) has not been explored for analyzing structural damage. Furthermore, there is no evidence of comparative studies of these two methods when applied to the problem of progressing structural damage. Thus, this paper presents a comparative analysis of these two methods in the context of identifying structural damage growth. The comparison is made based on the factors of success rate and training/testing times. Additionally, this approach has carried out the performance analysis of suitable parameters setup for SFAN: choice parameter, training rate and vigilance parameter. As a practical case study, both methods were applied to a unidirectional composite plate containing four PZT (Lead Zirconate Titanate) patches where the damage growth scenarios were also simulated by loosening bolts for three different levels. In addition, the repaired structural condition was also considered by retightening bolts. The results have shown that both methods are suitable for the problem of damage growth, particularly to supporting decisions about the structural damage assessment. In short, the comparative analysis has shown that the SFAN method is better suited to the problem of damage growth especially in regard to training and testing times. Thus, this comparative study contributes to helping researchers and industrialist in choosing more effective approaches for structural damage growth.