4.7 Article

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

期刊

LANDSLIDES
卷 13, 期 1, 页码 115-127

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10346-014-0551-4

关键词

Rock falls; Uncertainty; Quantitative risk assessment; Monte Carlo simulation

资金

  1. Railway Ground Hazards Research Program (RGHRP)
  2. Canadian Railway Research Laboratory (CaRRL)
  3. Natural Science and Engineering Research Council of Canada (NSERC)

向作者/读者索取更多资源

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.

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