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Geological Hazard Risk Evaluation for Railway Network of Guizhou Province in China

  • Rui TangEmail author
  • Weidong Wang
  • Jie Ma
  • Yanping Chen
Conference paper
Part of the Sustainable Civil Infrastructures book series (SUCI)

Abstract

In recent years, China’s high-speed railway has experienced a period of rapid development and being gradually rational. This paper took Guizhou Province as the study area, one of the places in China which are most seriously affected by landslide hazards. The research in this paper was conducted in three steps. Firstly, the landslide susceptibility mapping of railway was acquired by applying competition network model, and a set of conditioning factors were selected as the major landslide-conditioning factors, including elevation, lithology, rainfall, distance from river, distance from tectonic line, karst density and slope. Then, the concept of ‘degree of fitting’ was proposed in the assessment of railway risk degree, and it was regarded as one of the three elements which determine the railway protection grade on geological disasters. Finally, the matter-element model was established based on extension method, which can be used to evaluate the protection grades for the planned railway on geological disasters by integrating three elements, the train speed, grade of susceptibility mapping, and fitting degree, into the model.

Notes

Acknowledgement

The authors wish to acknowledge the support and motivation provided by Geological Hazard Susceptibility Mapping and Assessment in Basaltic Area (No: 2009318802074), and West Project of Ministry Communication, China (N0. 2009318000074).

References

  1. 1.
    Varnes, D.J.: Slope Movement Types and Processes. Landslides: Analysis and Control. Transportation Research Board Special Report. National Academy of Sciences (1978)Google Scholar
  2. 2.
    Pistocchi, A., et.al.: The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ. Geol. (2002). doi: 10.1007/s002540100440, Springer
  3. 3.
    Yesilnacar, E., Hunter, G.J.: Application of Neural Networks for Landslide Susceptibility Mapping in Turkey. Kluwer Academic Publishers, Springer, The Netherlands, Dordrecht (2004). doi: 10.1007/1-4020-2409-6_1
  4. 4.
    Melchiorre, C., et.al.: Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, ScienceDirect (2008). doi: 10.1016/j.geomorph.2006.10.035
  5. 5.
    Yin, K.L., et.al.: Early-warning and prediction of abrupt geological hazards in Zhejiang Province. China University of Geosciences Press (2005)Google Scholar
  6. 6.
    Dai, F.C., et.al.: Landslide risk assessment and management: an overview. Eng. Geol. ScienceDirect (2002). doi: 10.1016/s0013-7952(01)00093-x
  7. 7.
    Zhou, M., Ren, P.A.: Fitting degree of regression straight line. J. Xi’an Eng. Univ. (1999)Google Scholar
  8. 8.
    Li, W.L., Cui, J.K.: On parameter estimation of the largest fittings. J. Shaanxi Inst. Technol. (2002)Google Scholar
  9. 9.
    Fang, H.Y., Chen, J.J.: BP model for hydrologic series prediction and goodness of fit analysis. J. Yangzhou Univ. (2001)Google Scholar
  10. 10.
    Li, Y.F., Liu, S.J.: The matter element analytical method of the assement of rock mass stability. Gold (1998)Google Scholar
  11. 11.
    Zuo, C.Q., Chen, J.P.: Rock mass classification based on extenics theory applied in metamorphic soft rock tunnel. Geol. Sci. Technol. Inf. (2007)Google Scholar
  12. 12.
    Hao, H.C., Zhu, F.H.: Recognition method of potential failure mode of slope based on extenics theory. Chin. J. Undergr. Space Eng. 4, 024 (2007)Google Scholar
  13. 13.
    Wang, L.: Topology analysis for steadiness of side slope of rock body. Hebei Metall. 1, 21–23 (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Civil EngineeringCentral South UniversityChangshaChina
  2. 2.National Engineering Laboratory for High Speed Railway ConstructionChangshaChina
  3. 3.China Railway Eryuan Engineering Group Company LimitedChengduChina
  4. 4.China Railway Siyuan Survey and Design Group Company LimitedWuhanChina

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