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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2009, Vol. 10 Issue (1): 101-108    DOI: 10.1631/jzus.A0820265
Mechanical & Civil Engineering     
Assessment of highway slope failure using neural networks
Tsung-lin LEE, Hung-ming LIN, Yuh-pin LU
Department of Construction and Facility Management, Leader University, Tainan 709, China; Department of Resource and Environment, Leader University, Tainan 709, China
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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.

Key words： Neural network      Prediction      Highway      Slope failure     
Received: 05 January 2008     
CLC:  TU4  
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Tsung-lin LEE
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Yuh-pin LU
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Tsung-lin LEE, Hung-ming LIN, Yuh-pin LU. Assessment of highway slope failure using neural networks. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2009, 10(1): 101-108.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A0820265     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2009/V10/I1/101

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