Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline UV-CIPP rehabilitation materials based on machine learning
Yangyang Xia(National Clinical Research), Bokai Liu(Bauhaus-Universität Weimar), Yiming Yuan(Sichuan University), Hongyuan Fang(Zhengzhou University), Mingsheng Shi(Henan Province Water Conservancy Survey and Design Research), Bin Li, Chao Zhang(Zhengzhou University), Peng Zhao(Zhengzhou University), Hongjin Liu, Xinxin Sang(Jiangnan University), AN Guan-feng(Guangzhou Municipal Engineering Design and Research Institute), Ren Liu(Jiangnan University), Cuixia Wang(China Pharmaceutical University)
Cited by 75
Related Papers
Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites
|Composites Science and Technology|2022|191
A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms
|Composite Structures|2021|190
High‐Performance Neuromorphic Computing Based on Ferroelectric Synapses with Excellent Conductance Linearity and Symmetry
|Advanced Functional Materials|2022|188