LAUSR

Abstract The uncertainty information related to uncertain structural, material and geometric parameters is included in the available input uncertainty data, and there are multiple uncertainty types when only insufficient input data is acquired from experimental or numerical analyses. In this study, an uncertainty analysis method for composite laminated plate is proposed using a data-driven polynomial chaos expansion (PCE) method under insufficient input data related to uncertain design parameters. An identification criterion for uncertain types of design parameters is constructed based on the Akaike information criterion and observation significance level method, and the uncertain design parameters are subsequently divided into strong statistical variables, sparse variables, and interval variables. A data-driven PCE model of composite laminated plate is constructed by simultaneously considering the three uncertainty types, in which the polynomial coefficients are calculated based on the statistical moment of both strong statistical variables and sparse variables, and the PCE coefficients are quadratic polynomial functions of interval variables. The uncertainty analysis problems of the natural frequency of 5-layer and 10-layer composite laminated plates are presented to verify the effectiveness of the proposed algorithm.