Our mother nature has been providing human beings with numerous resources to inspire from, in building a finer life. Particularly in structural design, plenteous notions are being drawn from nature in enhancing the structural capacity as well as the appearance of the structures. Here plant stems, roots and various other structures available in nature that exhibit better buckling resistance are mimicked and modeled by finite element analysis to create a training database. The finite element analysis is validated by uniaxial compression to buckling of 3D printed biomimetic rods using a polymeric ink. After feature identification, forward design and data filtering are conducted by machine learning to optimize the biomimetic rods. The results show that the machine learning designed rods have 150% better buckling resistance than all the rods in the training database, i.e., better than the nature’s counterparts. It is expected that this study opens up a new opportunity to design engineering rods or columns with superior buckling resistance such as in bridges, buildings, and truss structures.

我们的大自然一直在为人类提供更多的灵感来源，以建立更美好的生活。特别是在结构设计中，从自然界中汲取了丰富的概念，以增强结构的能力以及结构的外观。在这里，通过有限元分析模拟并模拟了自然界中表现出更好的抗屈曲性的植物茎，根和其他各种结构，以创建训练数据库。有限元分析通过使用聚合物墨水对3D打印仿生棒进行单轴压缩屈曲来验证。在特征识别之后，通过机器学习进行前向设计和数据过滤，以优化仿生棒。结果表明，机器学习设计的杆的屈曲阻力比训练数据库中的所有杆高150％，即比自然界的杆更好。可以预期，这项研究为设计具有出色抗屈曲性的工程杆或柱（例如桥梁，建筑物和桁架结构）提供了新的机会。