Frequency-based delamination detection in stiffened fiber reinforced polymer composite plates

The present work focuses on assessment of delamination damage in stiffened fiber reinforced polymer (FRP) composite plates through the changes in frequencies after delamination occurring. The two inverse algorithms, namely artificial neural network (ANN) and surrogate assisted optimization (SAO) were developed to predict the location and size of delamination in the stiffened FRP plates using a series of frequency shifts. The efficiency and accuracy of the frequency-based detection algorithms were validated both numerically and experimentally. The results of numerical validation show that the two proposed inverse detection algorithms can successfully identify the delamination in stiffened FRP plates with good accuracy (maximum errors are 5.04% for ANN and 5.24% for SAO, both in the prediction of delamination location). Compared to using the genetic algorithm directly, SAO can greatly enhance the prediction efficiency and maintain good accuracy. The experimental results show that the prediction accuracy of ANN is reduced greatly compared with the numerical validation due to the existence of the measurement noise in the testing, and ANN cannot give useful information of delamination in stiffened FRP plate specimens. But SAO still can obtain reasonable prediction accuracy, with maximum prediction errors of 2.05% and 9% for through-flange delamination and the embeded delamination in the base plate, while two out of four specimens have predicted the delamination to have overlapping areas with the actual delamination, of which the overlapping areas are 34% and 32.65%. In conclusion, frequency-based method using ANN and SAO as inverse algorithms is validated numerically to successfully predict delamination, but only SAO can give reasonable prediction in delamination size and location when using the measured frequencies of the stiffened plate specimens.