A back-propagation network for the assessment of susceptibility to rock slope failure in the eastern portion of the Southern Cross-Island Highway in Taiwan

An assessment of the susceptibility to rock slope failure by means of a back-propagation network is proposed for the eastern portion of the Southern Cross-Island Highway in Taiwan. The model was developed on the basis of six influence parameters of rock slope instability, which include the rock type, slope aspect, slope angle, joint set number, joint spacing and bedding–slope relationship. The values of these influence parameters were used as inputs for the network and were classified as nominal scales in terms of binary numbers, while the state of failure/non-failure of a given slope was assumed to be the output variable. Data on a total of 170 slopes along the highway was fed into the network for learning. According to the outputs of the network, the susceptibility to rock slope failure is categorized into four levels, namely low, medium–low, medium and high, which are mapped along the highway. Three highly susceptible regions are found, which can be viewed as hazardous sections requiring cautionary measures. Moreover, the proposed model can be used as a tool for determining the possible state of an unfamiliar rock slope in the context of devising management strategies to be applied to the investigated portion of the highway.

[1]  C. F. Lee,et al.  Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .

[2]  A. Karakas Practical Rock Engineering , 2008 .

[3]  Robert J. Schalkoff,et al.  Artificial neural networks , 1997 .

[4]  Shou-Heng Liu,et al.  Impacts of the Chi-Chi earthquake on subsequent rainfall-induced landslides in central Taiwan , 2006 .

[5]  Uncertainty in ground motion estimates for the evaluation of slope stability during earthquakes , 2002, Quarterly Journal of Engineering Geology and Hydrogeology.

[6]  Jiun-Chuan Lin,et al.  Slope Movements in a Dynamic Environment - A case study of Tachia River, Central Taiwan , 2006 .

[7]  Manoj Pant,et al.  Landslide hazard mapping based on geological attributes , 1992 .

[8]  C. Chung,et al.  Multivariate Regression Analysis for Landslide Hazard Zonation , 1995 .

[9]  Mongkut Piantanakulchai,et al.  Analytic network process model for landslide hazard zonation , 2006 .

[10]  A. S. Al-Homoud,et al.  A classification system for the assessment of slope stability of terrains along highway routes in Jordan , 1998 .

[11]  Tien H. Wu,et al.  Prediction and mapping of landslide hazard , 2000 .

[12]  James S. Griffiths,et al.  Landslide susceptibility in the Río Aguas catchment, SE Spain , 2002, Quarterly Journal of Engineering Geology and Hydrogeology.

[13]  Saro Lee,et al.  Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea , 2003 .

[14]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[15]  Brian G. Lees,et al.  Neural network applications in the geosciences: An introduction , 1996 .

[16]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[17]  W. Marcus,et al.  Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho , 2001 .

[18]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[19]  D. Petley,et al.  Topographic controls on coseismic rock slides during the 1999 Chi-Chi earthquake, Taiwan , 2005, Quarterly Journal of Engineering Geology and Hydrogeology.

[20]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

[21]  D. Cruden,et al.  Hazardous Modes of Rock Slope Movement in the Canadian Rockies , 1996 .

[22]  R. Marston,et al.  Geoecology and mass movement in the Manaslu-Ganesh and Langtang-Jugal Himals, Nepal , 1998 .

[23]  Richard A. Roth Factors Affecting Landslide-Susceptibility in San Mateo County, California , 1983 .

[24]  P. Aleotti,et al.  Landslide hazard assessment: summary review and new perspectives , 1999 .

[25]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[26]  Fausto Guzzetti,et al.  Geographical Information Systems in Assessing Natural Hazards , 2010 .