Rainfall thresholds as a landslide indicator for engineered slopes on the Irish Rail network

Abstract

Rainfall thresholds express the minimum levels of rainfall that need to be reached or exceeded in order for landslides to occur in a particular area. They are a common tool in expressing the temporal portion of landslide hazard analysis. Numerous rainfall thresholds have been developed for different areas worldwide, however none of these are focused on landslides occurring on the engineered slopes on transport infrastructure networks. This paper uses empirical method to develop the rainfall thresholds for landslides on the Irish Rail network earthworks. For comparison, rainfall thresholds are also developed for natural terrain in Ireland. The results show that particular thresholds involving relatively low rainfall intensities are applicable for Ireland, owing to the specific climate. Furthermore, the comparison shows that rainfall thresholds for engineered slopes are lower than those for landslides occurring on the natural terrain. This has severe implications as it indicates that there is a significant risk involved when using generic weather alerts (developed largely for natural terrain) for infrastructure management, and showcases the need for developing railway and road specific rainfall thresholds for landslides.

On the nexus between landslide susceptibility and transport infrastructure – an agent-based approach

“…Since no context-free definition of road network vulnerability exists, the respective methodological approaches (even if highly sophisticated) remain fragmentary and repeatedly tailored to individual settings (Bagloee et al, 2017;Eidsvig et al, 2017;Mattsson and Jenelius, 2015;Rupi et al, 2015;Fuchs et al, 2013). Berdica (2002, p. 119), for example, suggested that network vulnerability should be understood as "susceptibility to incidents that can result in considerable reductions in road network serviceability".…”

“…In sum, the idea behind vulnera-bility is a decline in the original capacity to handle the network flow based on disruption (Yin and Xu, 2010). Nevertheless, in the literature two main directions within network vulnerability assessment can be distinguished: (1) topological vulnerability analysis, which includes the assessment of real transport network systems (represented in an abstract network); and (2) system-based vulnerability analysis, which focuses on the structure of the network within supply and demand models (Mattsson and Jenelius, 2015). In the context of the present paper, we understand vulnerability as the assessment of the disruptive impact based on a certain event (incident) which causes a malfunction or breakdown in the current road network system (Postance et al, 2017;Pregnolato et al, 2017;Klose et al, 2015;Mattsson and Jenelius, 2015).…”

“…Nevertheless, in the literature two main directions within network vulnerability assessment can be distinguished: (1) topological vulnerability analysis, which includes the assessment of real transport network systems (represented in an abstract network); and (2) system-based vulnerability analysis, which focuses on the structure of the network within supply and demand models (Mattsson and Jenelius, 2015). In the context of the present paper, we understand vulnerability as the assessment of the disruptive impact based on a certain event (incident) which causes a malfunction or breakdown in the current road network system (Postance et al, 2017;Pregnolato et al, 2017;Klose et al, 2015;Mattsson and Jenelius, 2015). The potential disruption may span from natural hazard events to terrorist attacks, infrastructure collapses or ordinary traffic accidents (Bagloee et al, 2017;Unterrader et al, 2018;Vera Valero et al, 2016;Mattsson and Jenelius, 2015;Koetse and Rietveld, 2009;Zischg et al, 2005b;Margreth et al, 2003).…”

“…In the context of the present paper, we understand vulnerability as the assessment of the disruptive impact based on a certain event (incident) which causes a malfunction or breakdown in the current road network system (Postance et al, 2017;Pregnolato et al, 2017;Klose et al, 2015;Mattsson and Jenelius, 2015). The potential disruption may span from natural hazard events to terrorist attacks, infrastructure collapses or ordinary traffic accidents (Bagloee et al, 2017;Unterrader et al, 2018;Vera Valero et al, 2016;Mattsson and Jenelius, 2015;Koetse and Rietveld, 2009;Zischg et al, 2005b;Margreth et al, 2003). Depending on the threat, the potential consequence can be additional travel time from some minutes to total cut-offs of several days of a community (Rupi et al, 2015;Taylor and Susilawati, 2012;Jenelius, 2009;Zischg et al, 2005b).…”

Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

“…In recent years, several data-driven methodologies were built to identify the susceptible sectors of a road network to landslides (Budetta, 2004;Hearn et al, 2008;Jaiswal et al, 2010aJaiswal et al, , b, 2011Quinn et al, 2010;Michoud et al, 2012;Tarolli et al, 2013;Bil et al, 2014Bil et al, , 2017Penna et al, 2014;Ramesh and Anbazhagan, 2015;Tarolli and Sofia, 2016;Winter et al, 2016;Donnini et al, 2017;Pellicani et al, 2017;Postance et al, 2017;Martinovic et al, 2018). These methods are based on quantitative statistical relationships between predisposing factors and a response variable.…”