Full Text:   <3923>

CLC number: TU4

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2008-10-29

Cited: 7

Clicked: 6917

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

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

http://doi.org/10.1631/jzus.A0820265


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.

@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"
}

%0 Journal Article
%T Assessment of highway slope failure using neural networks
%A Tsung-lin LEE
%A Hung-ming LIN
%A Yuh-pin LU
%J Journal of Zhejiang University SCIENCE A
%V 10
%N 1
%P 101-108
%@ 1673-565X
%D 2009
%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
ER -
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.

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

Reference

[1] Abrahart, R.J., See, L., Kneal, P.E., 2001. Investigating the role of saliency analysis with neural network rainfall-runoff model. Computers & Geosciences, 27(8):921-928.

[2] Caine, N., 1980. The rainfall intensity-duration control of shallow landslides and debris flow. Geografiska Annaler Series A, Physical Geography, 62(1/2):23-27.

[3] Campolo, M., Andreussi, P., Soldati, A., 1999. River flood forecasting with a neural network model. Water Resources Research, 35(4):1191-1197.

[4] Cannon, S.H., Ellen, S.D., 1985. Rainfall conditions for abundant debris avalanches in San Francisco Bay region, California. California Geology, 38(12):267-272.

[5] Deo, M.C., Naidu, C.S., 1998. Real time wave forecasting using neural networks. Ocean Engineering, 26(3):191-303.

[6] Derin, N.U., Hasan, S., 1998. Liquefaction assessment by artificial neural networks. The Electronic Journal of Geotechnical Engineering. Available from: http://www.ejge.com/1998/Ppr9803/Ppr9803.htm

[7] Fell, R., Hartford, D., 1997. Landslide Risk Management. In: Cruden, D., Fell, R. (Ed.), Landslide Risk Assessment, Balkema, Rotterdam, p.51-109.

[8] French, M.N., Krajewski, W.F., Cuykendall, R.R., 1992. Rainfall forecasting in space and time using a neural network. Journal of Hydrology, 137(1-4):1-31.

[9] Haykin, S., 1999. Neural Networks: A Comprehensive Foundation. Prentice-Hall, p.842.

[10] Hornik, K., 1993. Some new results on neural network approximation. Neural Networks, 6(9):1069-1072.

[11] Jacobs, R.A., 1988. Increased rates of convergence through learning rate adaptation. Neural Network, 1(4):295-307.

[12] Jeng, D.S., Lee, T.L., Lin, C., 2003. Assessment of Chi-Chi Earthquake-induced Liquefaction. Application of ANN Model. Proceedings Seventh Conference on Artificial Intelligence and Applications, p.50.

[13] Keefer, D.K., Wilson, R.C., Mark, R.K., Brabb, E.E., Brown III, W.M., Ellen, S.D., Harp, E.L., Wieczorek, G.F., Alger, C.S., Zatkin, R.S., 1987. Real-time landslide warning during heavy rainfall. Science, 238(4829):921-925.

[14] Lee, T.L., 2004. Back-propagation neural network for long-term tidal predictions. Ocean Engineering, 31(2):225-238.

[15] Lee, T.L., 2006. Neural network prediction of a storm surge. Ocean Engineering, 33(3-4):483-494.

[16] Lee, T.L., 2008. Back-propagation neural network for the prediction of the short term storm surge in Taichung harbor, Taiwan. Engineering Applications of Artificial Intelligence, 21(1):63-72.

[17] Lee, T.L., Jeng, D.S., 2002. Application of artificial neural networks in tide forecasting. Ocean Engineering, 29(9):1003-1022.

[18] Lee, S., Ryu, J., Min, K., Won, J., 2001. Development of two artificail neural network methods for landslide susceptibility analysis. Geoscience and Remote Sensing Symposium, 5:2364-2366.

[19] Maier, H.R., Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software, 15(1):101-124.

[20] Makarynskyy, O., 2004. Improving wave predictions with artificial neural networks. Ocean Engineering, 31(5-6):709-724.

[21] Makarynskyy, O., 2005. Artificial neural networks for wave tracking, retrieval and prediction. Pacific Oceanography, 3(1):21-30.

[22] Makarynskyy, O., Makarynska, D., Kuhn, M., Featherstone, W.E., 2004. Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine Coastal and Shelf Science, 61(2):351-360.

[23] Mase, H., Kianto, T., 1999. Prediction model for occurrence of impact force. Ocean Engineering, 26(10):949-961.

[24] Makarynskyy, O., Pires-Silva, A.A., Makarynska, D., Ventura-Soares, C., 2005. Artificial neural networks in wave predictions at the west coast of Portugal. Computers & Geosciences, 31:415-424.

[25] Muller, L., Hofman, H., 1970. Compilation and Assessment of Geological Data for the Slope Problem. International Symposium Open Pit Mining, Johannesburg, p.153-170.

[26] Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning representations by back-propagating errors. Nature, 323(6088):533-536.

[27] Varnes, D.J., 1978. Landslides Analysis and Control. Transportation, Res. Board Nat. Ac. Sci., Washington Spee. Rep., p.176.

[28] Yang, Y., Zhang, Q., 1998. The application of neural network to rock engineering systems (RES). International Journal of Rock Mechanics and Mining Sciences, 35(6):727-745.

[29] Zhang, Y.X., 1996. An artificial neural network for forecasting the amount of Chinese colliery roadway surrounding rock deformation. International Journal of Rock Mechanics and Mining Sciences & Geomechanics, 33(5):232A-232A(1).

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE