首页|Findings on Machine Learning Reported by Investigators at University of Science and Technology Beijing (Multi-damage Indexbased Interfacial Debonding Predictio n for Steel-concrete Composite Structures With Percussion Method)
Findings on Machine Learning Reported by Investigators at University of Science and Technology Beijing (Multi-damage Indexbased Interfacial Debonding Predictio n for Steel-concrete Composite Structures With Percussion Method)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Researchers detail new data in Machine Learning. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "The interfacial debonding as an invisi ble damage significantly undermines the bearing capacity and durability of steel -concrete composite structures (SCCS). Although the percussion method has been w idely utilized in practical applications, the single damage index (DI) extracted in the analysis process tends to be invalid to abnormalities and leads to misju dgment." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Beijing Municipal Science & Technology Commiss ion, Interdisciplinary Research Project for Young Teachers of USTB (Fundamental Research Funds for the Central Universities, Key Laboratory for Intelligent Infr astructure and Monitoring of Fujian Province (Huaqiao University).
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningUniversity of Science and T echnology Beijing
NETL
NSTL
