A probabilistic approach for quantitative identification of multiple delaminations in laminated composite beams using guided waves. (15th November 2016) - He, Shuai; Ng, Ching-Tai
Highlights: Bayesian approach is proposed to quantitatively identify multiple delaminations. The associated uncertainties of identified delamination parameters are quantified. Subset simulation is employed to enhance the robustness in the identification. Spectral finite element method is used to provide computational efficient simulations. Numerical and experimental case studies using laser vibrometre are provided. Abstract: In this study a probabilistic approach is proposed to identify multiple delaminations in laminated composite beams using guided waves. The proposed method is a model-based approach, which provides a quantitative identification of the delaminations. This study puts forward a practical damage identification method, and hence, it can identify multiple delaminations using guided wave signal measured at a single measurement point on the laminated composite beams. The proposed method first determines the number of delaminations using Bayesian model class selection method. The Bayesian statistical framework is then employed to not only identify the delamination locations, lengths and through-thickness locations, but also quantify the associated uncertainties, which provides valuable information for engineers in making decision on necessary remedial work. In addition the proposed method employs the time-domain spectral finite element method and Bayesian updating with Subset simulation to further improve the computational efficiency. The proposed probabilistic approach is verified and demonstrated using data obtained from numerical simulations, which consider both measurement noise and modeling error, and experimental data. The results show that the proposed method can accurately determine the number of delaminations, and the identified delamination locations, lengths and through-thickness locations are closed to the true values.