A study was conducted on the changes in mechanical properties and fatigue failure characteristics due to voids, one of the fabrication defects in composite materials. This study was primarily based on glass fiber fabrics applied to wind turbine blades and their material properties were predicted through micro and meso modeling of composite materials by simulating random defects including voids. As the wind turbine blades become larger, various defects develop. The fundamental changes in the materials’ properties due to voids were predicted through homogenization methods and Representative Volume Element (RVE), and the failure properties were obtained through progressive failure analysis by applying virtual coupons according to ASTM D3090 and ASTM D6641. The progressive failure was identified using the Matzenmiller–Lubliner–Taylor (MLT) failure condition, and the fatigue failure characteristics were assessed through the Tsai–Hill 3D load.

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design’s performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.