Reliability analysis based on polynomial chaos expansion method in composite structures

Reliability analysis of composite structures has gained increased attention due to the growing use of composite materials in many industries such as aerospace, automotive and construction in recent decades. Uncertainty analysis approaches are effective tools in order to probabilistically assess the behavior and evaluate the reliability of composite structures with variabilities in material properties. In this study, a computationally efficient surrogate model based on the polynomial chaos expansion for reliability analysis of composite structures with a large number of uncertain parameters is presented. The uncertain input parameters including composite material properties, geometry and loads, are assumed as random variables with a normal distribution and are taken into account for reliability evaluation. A sparse grid collocation strategy is used to determine the sample points for constructing the surrogate model relating the uncertain variables to the structural response. In the end, a numerical example is performed to demonstrate the accuracy and efficiency of this methodology for a higher number of uncertain variables by comparing the results with the direct Monte Carlo simulation method.