A modified transmissibility indicator and Artificial Neural Network for damage identification and quantification in laminated composite structures.

Recently, more attention has been paid to Artificial Neural Network (ANN) in the field of damage identification of engineering structures based on modal analysis. This paper proposes a new modified damage indicator, using transmissibility technique to improve Local Frequency Response Ratio (LFCR), combined with ANN. The main objective of the proposed damage indicator is to reduce the number of collected data for fast prediction and with higher accuracy instead of collecting all modal analysis data, i.e. natural frequencies, damping ratios, and mode shapes, or using inverse analysis for damage quantification. The suggested approach is tested using three layers laminated cross-ply [0°/90°/0°] composite beam and plate having single and multiple damage(s). The reliability and accuracy of the proposed application are demonstrated by predicting the severity of damages in the considered composite structures after analysing four damage scenarios. [ABSTRACT FROM AUTHOR]