, Volume 13, Issue 1, pp 115–127 | Cite as

Quantitative risk assessment of slope hazards along a section of railway in the Canadian Cordillera—a methodology considering the uncertainty in the results

  • Renato MacciottaEmail author
  • C. Derek Martin
  • Norbert R. Morgenstern
  • David M. Cruden
Original Paper


Railway alignments through the Canadian Cordillera are constantly exposed to slope instabilities. Proactive mitigation strategies have been in place for a few decades now, and instability record keeping has been recognized as an important aspect of them. Such a proactive strategy has enhanced the industry’s capacity to manage slope risks, and some sections have been recognized as critical due to the frequency of instabilities. At these locations, quantification of the risks becomes necessary. Risk analysis requires knowledge of some variables for which statistical data are scarce or not available, and elicitation of subjective probabilities is needed. A limitation of such approaches lies in the uncertainty associated to those elicited probabilities. In this paper, a quantitative risk analysis is presented for a section of railway across the Canadian Cordillera. The analysis focused on the risk to life of the freight train crews working along this section. Upper and lower bounds were elicited to cope with the uncertainties associated with this approach. A Monte Carlo simulation technique was then applied to obtain the probability distribution of the estimated risks. The risk probability distribution suggests that the risk to life of the crews is below previously published evaluation criteria and within acceptable levels. The risk assessment approach proposed focuses on providing a measure of the uncertainty associated with the estimated risk and is capable of handling distributions that cover more than two orders of magnitude.


Rock falls Uncertainty Quantitative risk assessment Monte Carlo simulation 



The authors wish to acknowledge the Railway Ground Hazards Research Program (RGHRP), the Canadian Railway Research Laboratory (CaRRL), and the Natural Science and Engineering Research Council of Canada (NSERC) for funding this study. The first author wishes to acknowledge the assistance of Dr. Simaan Abourizk at the University of Alberta, Department of Civil and Environmental Engineering for his insights into simulation through Monte Carlo techniques.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Renato Macciotta
    • 1
    Email author
  • C. Derek Martin
    • 2
  • Norbert R. Morgenstern
    • 3
  • David M. Cruden
    • 4
  1. 1.Department of Civil and Environmental EngineeringUniversity of AlbertaEdmontonCanada
  2. 2.Canadian Rail Research Laboratory, Department of Civil and Environmental EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Department of Civil and Environmental EngineeringUniversity of AlbertaEdmontonCanada
  4. 4.Department of Civil and Environmental EngineeringUniversity of AlbertaEdmontonCanada

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