Prediction for Relative Dynamic Elastic Modulus of PVA-ECC under Freezing and Thawing Cycles

Abstract:

This study focuses on the relative dynamic elastic modulus of the polyvinyl alcohol fiber reinforced cementitious composite (PVA-ECC) after three hundred freeze-thaw cycles. The Artificial Neural Network of freeze-thaw cycles prediction was finally established through data analysis with the help of BP artificial neural network, calculation method optimization and sample training, many times’ trails of the hidden layer and every hidden unit, and the optimal selection of the training function. The results show that there is a small relative error between the predicted value and the actual one of the specimen of the relative dynamic elastic modulus of the PVA-ECC, and the established artificial neural network model bears a higher prediction precision.

• Periodical: Applied Mechanics and Materials (Volumes 44-47)
• Pages: 3893-3896
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.3893
• Online since: December 2010
• Authors: Hui Fang Hou, Shu Guang Liu, Chang Wang Yan, Jian Wen Bai
• Keywords: BP Artificial Neural Network, Freeze-Thaw Cycle, PVA-ECC, Relative Dynamic Elastic Modulus

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