摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据NewsRx记者从湖南发回的新闻报道，研究表明：“本研究采用实验、有限元(FE)模拟、机器学习(ML)和理论技术，研究了混合分层方形蜂窝(HHSHs)的平面内弹性模量(E HSH)。首先，利用三维打印机制作了不同构型的HSH，并对其进行了平面准静态压缩试验。本报编辑引用了土木工程学院的一篇研究文章，“然后，用234个有限元模型模拟了不同构型的hsh，并将结果用于11个ML模型的训练，对比分析表明，极梯度Boosting(XGBoost)模型具有最好的预测能力，而且，该模型具有较高的预测精度。”摘要:在XGBoost模型和现有理论的基础上,提出了一种改进的E HSH理论,并与实验结果、有限元分析结果和已有的理论结果进行了比较,验证了该理论的优越性。最后,利用该理论确定了E HSH的上、下界。并利用Shapley加法解释(SHAP)方法识别了不同几何参数对裁剪E HHSH的重要性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating from Hunan, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “In this stu dy, experimental, finite element (FE) simulation, machine learning (ML), and the oretical techniques are employed to investigate the in -plane elastic modulus ( E HHSH ) of hybrid hierarchical square honeycombs (HHSHs). First, HHSHs with dif ferent configurations were fabricated using a 3D printer, and in -plane quasi -s tatic compression tests were conducted on them.” Our news editors obtained a quote from the research from the School of Civil Eng ineering, “Then, 234 FE models are simulated to determine the E HHSH of HHSHs wi th various configurations, and the results are used to train 11 ML models. Compa rative analysis demonstrates that the Extreme Gradient Boosting (XGBoost) model has the best predictive capability. Moreover, a modified theory for E HHSH is es tablished based on the XGBoost model and existing theory, and its exceptional pr edictive capability is verified by comparing with experimental, FE, and existing theoretical results. Finally, the upper and lower bounds of E HHSH are determin ed by the modified theory, and the Shapley Additive Explanation (SHAP) method is used to identify the importance of different geometric parameters on tailoring E HHSH.”
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