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Received: 2008-01-05

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.1 P.101~108


Assessment of highway slope failure using neural networks

Author(s):  Tsung-lin LEE, Hung-ming LIN, Yuh-pin LU

Affiliation(s):  Department of Construction and Facility Management, Leader University, Tainan 709, China; more

Corresponding email(s):   tllee@mail.leader.edu.tw

Key Words:  Neural network, Prediction, Highway, Slope failure

Tsung-lin LEE, Hung-ming LIN, Yuh-pin LU. Assessment of highway slope failure using neural networks[J]. Journal of Zhejiang University Science A, 2009, 10(1): 101~108.

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author="Tsung-lin LEE, Hung-ming LIN, Yuh-pin LU",
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%T Assessment of highway slope failure using neural networks
%A Tsung-lin LEE
%A Hung-ming LIN
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%I Zhejiang University Press & Springer
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T1 - Assessment of highway slope failure using neural networks
A1 - Tsung-lin LEE
A1 - Hung-ming LIN
A1 - Yuh-pin LU
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 1
SP - 101
EP - 108
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820265

An artificial intelligence technique of back-propagation neural networks is used to assess the slope failure. On-site slope failure data from the South Cross-Island highway in southern Taiwan are used to test the performance of the neural network model. The numerical results demonstrate the effectiveness of artificial neural networks in the evaluation of slope failure potential based on five major factors, such as the slope gradient angle, the slope height, the cumulative precipitation, daily rainfall and strength of materials.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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