Zhifang Zhang

TL;DR: In this paper, the authors examined three different inverse algorithms for solving the non-linear equations to predict the interface, lengthwise location and size of delamination: direct of solution using a graphical method, artificial neural network (ANN) and surrogate-based optimization.

TL;DR: Delamination is a common damage in fiber reinforced composite laminates that can substantially reduce the structural stiffness which changes the dynamic response as discussed by the authors, which can be considered as a form of structural deformation.

TL;DR: In this paper, a novel concept of Noise Response Rate (NRR) is proposed to evaluate the sensitivity of each mode of the frequency shift to noise, and it is shown that selecting the vibration modes with low NRR values improves the prediction accuracy of frequency-based damage detection.

TL;DR: In this paper, a new surrogate assisted optimisation (SAO) method was proposed to predict the location and size of delaminations in fiber reinforced composite plates using natural frequency shifts as indicative parameters.

TL;DR: A delamination prediction strategy via K-means clustering, ANN and optimization algorithms integrated with surrogate models based on ANN for computational enhancement have been successfully developed and found efficient for detection of the interface of delamination, its size and location in FRP composite laminates using variations in natural frequencies.