In this paper, a stochastic homogenization method that couples the state-of-the-art computational multi-scale homogenization method with the stochastic finite element method, is proposed to predict the statistics of the effective elastic properties of textile composite materials. Uncertainties associated with the elastic properties of the constituents are considered. Accurately modeling the fabric reinforcement plays an important role in the prediction of the effective elastic properties of textile composites due to their complex structure. The p-version finite element method is adopted to refine the analysis. Performance of the proposed method is assessed by comparing the mean values and coefficients of variation for components of the effective elastic tensor obtained from the present method against corresponding results calculated by using Monte Carlo simulation method for a plain-weave textile composite. Results show that the proposed method has sufficient accuracy to capture the variability in effective elastic properties of the composite induced by the variation of the material properties of the constituents

This study sheds considerable light on the potential of superelastic shape memory alloy Belleville washers for innovative seismic resisting applications. A series of experimental studies were conducted on washers with different stack combinations under varying temperatures and loading scenarios. The washers showed satisfactory self-centring and energy dissipation capacities at room temperature, although slight degradations of the hysteretic responses accompanied by residual deformations were induced. The hysteretic loops became stable after a few number of cycles, indicating good repeatability. The washers also showed good flexibility in terms of load resistance and deformation, which could be easily varied via changes in the stack combination. Compromised self-centring responses were observed at temperatures below 0 °C or above 40 °C, and a numerical study, validated by the experimental results, was adopted to further investigate the deformation mechanism of the washers. A further phenomenological model, taking account of the degradation effects under varied temperatures, was developed to enable effective and accurate simulation of devices incorporating the washers. Good agreements were observed between the test and simulation results, and the model was shown to have good numerical robustness for wide engineering applications.

This paper investigates two critical issues, namely propagation of multi-scale uncertainty, and selection of failure criteria, related to reliability analysis of composites by using multi-scale methods. Due to the multi-scale architecture of composites, uncertainties exist in both microscale and macroscale parameters. It is necessary, therefore, to consider random variables at various length scales to ensure accurate estimates of the reliability of composites. Three types of homogenization methods, namely rule of mixtures, Mori–Tanaka and computational homogenization, are adopted to link these two scales, and to propagate uncertainty from micro to macro scales. By integrating these homogenization methods with the stochastic finite element method and structural reliability methods, the reliability of composites can be investigated with a limit state function based on a chosen failure criterion. This multi-scale reliability analysis procedure has been applied to analyse laminated fibre reinforced composites made of AS4/3501 carbon/epoxy. Firstly, a comparative study has been conducted to evaluate the performance of the assumed homogenization methods for the reliability of composites, and to identify advantages compared with a single scale analysis. The results show that multi-scale analysis can provide more accurate reliability estimates. Secondly, several popularly used failure criteria for composites have been compared using multi-scale reliability analysis