首页|Findings from School of Civil Engineering in the Area of Machine Learning Report ed (Machine Learning Assisted Prediction and Analysis of In-plane Elastic Modulu s of Hybrid Hierarchical Square Honeycombs)
Findings from School of Civil Engineering in the Area of Machine Learning Report ed (Machine Learning Assisted Prediction and Analysis of In-plane Elastic Modulu s of Hybrid Hierarchical Square Honeycombs)
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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.”
HunanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningSchool of Civil Engineering
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