聚酯玻璃钢大气老化力学性能BP人工神经网络预报模型的建立

杜武青; 刘颖慧; 赵晴; 王莹; 马达兵

Establishment of Prediction Model for GFRP?s Mechanical Property in Atmosphere Aging Based on BP Neural Network

Received:December 01, 2016 Revised:May 15, 2017

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DOI：10.7643/ issn.1672-9242.2017.04.021

KeyWord:unsaturated fiberglass reinforced plastic atmospheric aging artificial neural network

Author	Institution
DU Wu-qing	Department of Materials and Environment, ZHBIT, Zhuhai , China
LIU Ying-hui	Department of Materials and Environment, ZHBIT, Zhuhai , China
ZHAO Qing	Department of Material Science and Engineering, Nanchang HangKong University, Nanchang , China
WANG Ying	Department of Materials and Environment, ZHBIT, Zhuhai , China
MA Da-bing	Troops 91515 of PLA, Sanya , China

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Abstract:

Objective To analyze accurately and establish an aging model to explore conditions of fiberglass reinforced plastic in atmospheric environment and relations in change of its aging properties. Methods Changes of fiberglass reinforced plastic’s mechanical properties in different atmospheric environments of different seasons were analyzed based on the meteorological data and a model was established with artificial intelligence method-BP neural network of mathematical modeling. Results The actual value and predicted values were in good agreement and the model accuracy was high. Conclusion The prediction model can evaluate aging behaviors of unsaturated fiberglass reinforced plastic in the atmosphere accurately.