CLC number: TU4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2008-10-29
Cited: 7
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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.
@article{title="Assessment of highway slope failure using neural networks",
author="Tsung-lin LEE, Hung-ming LIN, Yuh-pin LU",
journal="Journal of Zhejiang University Science A",
volume="10",
number="1",
pages="101-108",
year="2009",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820265"
}
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%A Hung-ming LIN
%A Yuh-pin LU
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820265
TY - JOUR
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
Abstract: 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.
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