Development of a landslide susceptibility assessment for a rail network

Karlo Martinović, Kenneth Gavin, Cormac Reale

Research output: Contribution to journalArticlepeer-review

48 Citations (SciVal)

Abstract

This paper examines the applicability of a landslide susceptibility assessment approach to engineered slopes using data from the Irish Rail network. A logistical regression model was used to determine the susceptibility of landslide occurrence on an asset by asset basis using input factors derived specifically for man-made earthworks. Records of past failures were used to train the model to predict the probability of future failures occurring. The model was used to analyse a substantial section of the Irish Rail network comprising of 1184 slopes. The database of assets was split into training and validation datasets and similar levels of predictive performance were achieved with both datasets indicating the applicability and robustness of the approach. The results of the study show that simple asset databases, partially populated by visual survey data, can be used effectively to carry out a landslide susceptibility analysis. This enables proactive identification of critical assets as opposed to the current reactive industry standard, which represents an important step forward in creating objective risk rating systems for transport network earthworks.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalEngineering Geology
Volume215
Early online date21 Oct 2016
DOIs
Publication statusPublished - 19 Dec 2016

Funding

The research is supported by the Irish Research Council Employment Based Postgraduate Programme and the Horizon 2020 Project Destination Rail (project no 636285 ). The authors would like to thank Cathal Mangan and Sharon Callanan from Irish Rail for the permission to use the data.

Keywords

  • Engineered slopes
  • Landslide susceptibility
  • Logistic regression
  • Rail
  • Road
  • Shallow slides

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geology

Fingerprint

Dive into the research topics of 'Development of a landslide susceptibility assessment for a rail network'. Together they form a unique fingerprint.

Cite this