| description abstract | The vibration correlation technique (VCT) has emerged as an advanced and nondestructive approach for structural buckling analysis, offering substantial potential for high-level engineering applications. This study aims to estimate the buckling load of a sandwich plate with a novel circular cell core using VCT. The sandwich plate is designed based on a unit cell characterized by three adjustable parameters. Machine learning algorithms (MLAs) are initially employed to model the relationship between these design parameters (inputs) and key performance indicators (outputs), namely, the specific buckling load to weight ratio (p0/m=pc) and the first specific natural frequency to weight ratio (f0/m=fc). The resulting predictive models are integrated into a genetic algorithm (GA) as an objective function to maximize both (pc) and (fc) simultaneously, enabling the identification of optimal design parameters. Subsequently, a homogenization method is applied to determine the equivalent mechanical properties of the circular cell core. The structural behavior is further analyzed using first-order shear deformation theory, with buckling and free vibration equations solved through a meshless numerical method. The VCT is then implemented to estimate the buckling load, and its predictions are validated against experimental results. Among the three VCT variants, the third method demonstrates superior accuracy, achieving a prediction error of less than 6.2%, thereby proving its efficiency over alternative approaches. This integrated framework highlights the synergy of machine learning, optimization algorithms, and advanced structural theories in achieving robust and efficient structural designs. This study introduces an advanced method for predicting the buckling load of sandwich plates, which are commonly used in engineering structures. By applying the vibration correlation technique (VCT), the research estimates the critical load a structure can withstand before buckling. The sandwich plate is designed with a unique circular cell core and optimized using MLAs to find the best design parameters. These algorithms help predict key factors such as the ratio of buckling load to weight and natural frequency to weight. The design is further refined using a GA to maximize both factors at the same time. To ensure accurate predictions, a method is used to calculate the material properties of the core, and the structural behavior is analyzed with advanced theories. The buckling load is then estimated with the VCT and validated by experimental tests. The results show that one variant of the VCT method offers highly accurate predictions with less than 6.2% error, demonstrating its potential to improve the design of lightweight yet strong structural components for real-world applications. | |
