Extended BP Network-Based Pavement and Road Settlement Prediction

Article Preview

Abstract:

In recent years, many approaches were proposed to deal with the settlement prediction problem. However, because of the complexity of geography and other conditions, the current solutions still have some deficiencies and do not work well. To improve the accuracy of settlement prediction, the authors of this paper propose an extended BP network-based approach. This method first designed the model of BP network according to the dataset. This network includes one input layer, one output layer, one middle layer and an extended middle layer. After trained by samples, this network is used to predict the settlement value. The experiment indicates that our approach is available and work well in most situations.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

499-502

Citation:

Online since:

December 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] A. Loulizi, I. L. AI-Qadi, and S. Lahouar, Optimization of Ground-Penetrating Radar data to predict layer thicknesses in flexiblepavements, Joumal of Transportation Engineering, Vol. 129, No. 1(2003) 93-99.

DOI: 10.1061/(asce)0733-947x(2003)129:1(93)

Google Scholar

[2] Huang Tao, He Yu-long, Liu Hui, Study on Mechanism of dynamicconsolidation control high-filled ground,. Road foundation engineering, Vol 33, (2007)43-45.

Google Scholar

[3] Song C Z, Wang L, Xie N G. Research on Neural Networks Training Based on Ant Colony Optimization[J]. Automation & Instrumentation, (2006)10-12.

Google Scholar

[4] Duan H B, Wang D B. A Novel Improved Ant Colony Algorithm with Fast Global Optimization and its Simulation [J]. Information and Control, (2004)241-244.

Google Scholar

[5] Liu Xin-ping, Tang Lei, Jin Youhai. Extending hidden-layer backpropagation neural work and its training algorithm. Computer integrated manufacturing systems. Vol. 14, No. 11, (2008)2284-2288.

Google Scholar

[6] Peng Zhao-qin, Cao Chun, Huang Jiang-ying, Liu Qiu-Sheng. seismic signal recognition using improved BP Neural network and combined feature extraction method. Journal of central south university, vol(21), No. 5, (2004)1898-(1906).

DOI: 10.1007/s11771-014-2136-8

Google Scholar

[7] Gyungja Jung, Jonghong Jung, Sung-Min Cho, Hongjong Kim. Evaluation of Road Settlements on Soft Groundfrom GPR Investigations. Tenth International Conference on Ground Penetrating Radar, (2010)651-654.

Google Scholar