Xinjie Zhang

This paper proposes an improved reliability analysis method for composite structures. A new uncertainty model is introduced to quantify the uncertainty in the elastic properties and strength characteristics of the constituents. A multi-scale analysis procedure is developed to link scales in elastic properties, and solve the mechanical responses of all scales. A set of limit state functions are defined, corresponding to the failure modes of intralaminar constituents’ failure and interlaminar delamination. First-order reliability method presents good convergence and accuracy of reliability estimation. Numerical cases are studied to demonstrate the validity and improvements of the proposed method.

A unidirectional convolutional neural network (UCNN)-based approach is proposed in this paper to take advantage of frequency response (FR) data for finite element model updating. UCNN is designed to acquire the high-precision inverse mapping from the FR data to the updating parameters without any artificial feature extraction. Unidirectional convolution is applied at the frequency and location dimensions of the FR data separately to avoid data coupling. UCNN outperforms the residual-based model updating method and two-dimensional convolutional neural networks on a satellite model updating experiment. It achieves highly- precision results both inside and outside of the training set.