Buckling behaviors prediction of biological staggered composites with finite element analysis and machine learning coupled method

Staggered structure, where mineral crystals are arranged in a staggered manner in protein matrix, is the most representative microstructure in biological composites. Because of the elongated geometry and large aspect ratio of mineral crystals, their failure mechanism under compressive loading is predominantly controlled by buckling. The microstructural complexity, diversity of the constituent materials, and the complexity of buckling behaviors present substantial challenges for the precise forecasting of the buckling behavior of biological materials. In this paper, a novel method combining finite element analysis with machine learning is proposed. Specifically, based on the large dataset obtained using the finite element method, the buckling strength of biological staggered composites was rapidly predicted using a machine learning algorithm. Herein, the effects of microstructure and component materials on buckling behavior have been investigated systematically. The results indicate that the neural network models are highly accurate in predicting critical buckling strength. Aspect ratio and modulus ratio significantly affect the critical buckling strength of staggered structures according to the results of feature importance analysis. Additionally, the post-buckling modes of staggered structures exhibit heightened sensitivity to initial geometric defects, particularly in higher-order buckling modes. Our research provides valuable insights into fast and accurate prediction of the buckling behavior of biological composites and their design.