Optimization of feed-forward neural networks configuration used for bridge condition rating approximation

Authors:
R. Hamid;Khairullah Yusuf;Abdul Khalim Abdul Rashid
Affiliations:
Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia Bangi, Selangor, Malaysia;Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia Bangi, Selangor, Malaysia;Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia Bangi, Selangor, Malaysia
Venue:
EMESEG'10 Proceedings of the 3rd WSEAS international conference on Engineering mechanics, structures, engineering geology
Year:
2010

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Abstract

The paper presents result from experiments on network architecture and transfer functions configuration in the feed-forward neural networks (FFNNs) applied to bridge condition rating approximation. Trial and error approach is done on three layers feed-forward neural network by varying the number of neurons in hidden layer. Levenberg-Marquardt training algorithm (trainlm) and sigmoid transfer function are applied in FFNN to investigate the best configuration to be used for bridge condition rating. Mean square error (MSE) and correlation coefficient (R) are used to measure the network performance. The results indicated that the configuration of FFNN with thirty-one neurons in hidden layer using tangent-sigmoid (tansig) transfer function in output layer have produced the best MSE and R than other configurations.