期刊
FRONTIERS IN BUILT ENVIRONMENT
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fbuil.2022.855112
关键词
machine learning; regression models; composite beams; orthotropic material model; higher-order beam theories
A comparative study of machine learning regression algorithms was conducted to predict the deflection of laminated composite beams. Ample numerically prepared data were used to overcome the scarcity of experimental data. The results show that the Extra-Trees algorithm performs best, demonstrating excellent predictive capabilities.
A comparative study of machine learning regression algorithms for predicting the deflection of laminated composite beams is presented herein. The problem of the scarcity of experimental data is solved by ample numerically prepared data, which are necessary for the training, validation, and testing of the algorithms. To this end, the pertinent geometric and material properties of the beam are discretized appropriately, and a refined higher-order beam theory is employed for the accurate evaluation of the deflection in each case. The results indicate that the Extra-Trees algorithm performs best, demonstrating excellent predictive capabilities.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据