NEURAL NETWORK PREDICTION OF THE HIGH-FILL ROAD FOUNDATION SETTLEMENT OF HIGHWAY

WANG Xiangqiu; YANG Linde; GAO Wenhua; CHEN Qiunan

Volume 12 Issue 4

Apr. 2011

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WANG Xiangqiu, YANG Linde, GAO Wenhua, CHEN Qiunan. 2004: NEURAL NETWORK PREDICTION OF THE HIGH-FILL ROAD FOUNDATION SETTLEMENT OF HIGHWAY. JOURNAL OF ENGINEERING GEOLOGY, 12(4): 427-430.

Citation:

WANG Xiangqiu, YANG Linde, GAO Wenhua, CHEN Qiunan. 2004: NEURAL NETWORK PREDICTION OF THE HIGH-FILL ROAD FOUNDATION SETTLEMENT OF HIGHWAY. JOURNAL OF ENGINEERING GEOLOGY, 12(4): 427-430.

WANG Xiangqiu, YANG Linde, GAO Wenhua, CHEN Qiunan. 2004: NEURAL NETWORK PREDICTION OF THE HIGH-FILL ROAD FOUNDATION SETTLEMENT OF HIGHWAY. JOURNAL OF ENGINEERING GEOLOGY, 12(4): 427-430.

Citation:

WANG Xiangqiu, YANG Linde, GAO Wenhua, CHEN Qiunan. 2004: NEURAL NETWORK PREDICTION OF THE HIGH-FILL ROAD FOUNDATION SETTLEMENT OF HIGHWAY. JOURNAL OF ENGINEERING GEOLOGY, 12(4): 427-430.

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NEURAL NETWORK PREDICTION OF THE HIGH-FILL ROAD FOUNDATION SETTLEMENT OF HIGHWAY

1.
Department of Civil Engineering, Hunan University of Science and Technology,Xiangtan 411201;
2.
Department of Geotechnical Engineering, Tongji University,Shanghai 200092

Funds:

Received Date: 2003-12-25
Rev Recd Date: 2004-03-12
Publish Date: 2004-12-15

Abstract

Abstract

Through use of the stronger nonlinear mapping and learning ability of the back propagation neural network, the authors develop a new artificial neural network model to predict the settlement of highway foundation. This model avoids the errors caused by artificial factors during calculation since the model is established using all in-situ observation data. The results show that the model simulation matches well with in-situ observation of the highway foundation settlement, which demonstrates its applicability in the engineering practice.
- Artificial neural network,
- Prediction model,
- Foundation of a highway,
- Settlement

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References

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