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Estimation of wood failure percentage under shear stress in bamboo-wood composite bonded by adhesive using a deep learning and entropy weight method

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成果类型:
期刊论文
作者:
Yang, Bin;Wu, Xinfeng;Hao, Jingxin;Xu, Dapeng;Liu, Tuoyu;...
通讯作者:
Xinfeng Wu
作者机构:
[Wu, Xinfeng; Liu, Tuoyu; Xu, Dapeng; Yang, Bin; Xie, Qingyu] Cent South Univ Forestry & Technol, Coll Mat Sci & Engn, Changsha 410004, Peoples R China.
[Hao, Jingxin] Cent South Univ Forestry & Technol, Coll Furniture & Art Design, Changsha 410004, Peoples R China.
通讯机构:
[Xinfeng Wu] C
College of Material Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China
语种:
英文
关键词:
Convolutional neural network;Deep learning;Entropy weight method;Measurement;Wood failure percentage
期刊:
Industrial Crops and Products
ISSN:
0926-6690
年:
2023
卷:
197
页码:
116617
基金类别:
This research was funded by the Hunan Provincial Department of Education Outstanding Youth Fund, grant number 22B0277 , National College Students Innovation and Entrepreneurship Training Program , grant number 202110538010 , and by Key Research and Development Plan of Hunan Province , grant number 2016NK2156 .
机构署名:
本校为第一机构
院系归属:
材料科学与工程学院
家具与艺术设计学院
摘要:
Wood failure percentage (WFP) is one of most important indices to evaluate the shear properties for wood-based composite bonded by adhesive, especially for cross-laminated timber. A new WFP measurement method for wood-bamboo composite bonded by phenolic resin (PF) and methylene diphenyl diisocyanate (MDI) separately under shear stress was proposed by combing image processing and deep learning (DL): its accuracy was corrected by the entropy weight method. The results show that the WFP measured by the DL method is in good agreement with the experimental value, however, the experimental condition...

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