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Proceedings Paper

Prediction of fatigue crack propagation rate on the interface of wood-FRP using the artificial neural network (ANN)
Author(s): Liang Zhang; Junhui Jia; Yongjun Liu
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Paper Abstract

Crack propagation rate of the interface of fiber reinforced polymer (FRP) bonded to red maple wood, is analyzed and predicted using an artificial neural network (ANN) method. The performance of Multilayer Perceptron (MLP) and Modular Neural Network (MNN) is compared to obtain an optimal ANN model to predict the crack propagation rate. The effect of various parameters of the MNN and MLP models are investigated. The number of input vectors of MLP and MNN models is studied to see if this will affect the training and predicting performance by the scatter of input vectors. At last, a new method called sensitivity analysis is adopted to explore the influenced proportion of the input vectors and the effect of load ratio, frequency, et al., on the crack propagation rate.

Paper Details

Date Published: 27 August 2009
PDF: 7 pages
Proc. SPIE 7375, ICEM 2008: International Conference on Experimental Mechanics 2008, 737513 (27 August 2009); doi: 10.1117/12.839046
Show Author Affiliations
Liang Zhang, Shenyang Jianzhu Univ. (China)
Junhui Jia, Goldreich Engineering, P.C. (United States)
Yongjun Liu, Shenyang Jianzhu Univ. (China)

Published in SPIE Proceedings Vol. 7375:
ICEM 2008: International Conference on Experimental Mechanics 2008
Xiaoyuan He; Huimin Xie; YiLan Kang, Editor(s)

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