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.

Many of the earthworks assets on rail transport networks were constructed in the 1800s and have thus operated for periods far in excess of their expected service life. Incidences of failure — particularly shallow planar landslides — are increasing, in part due to the effect of more intense and longer duration rainfall events. Network owners have difficulty in targeting scarce resources to reduce risk across networks. This paper proposes a methodology for developing fragility curves for rainfallinduced landslides on transport networks. Fragility curves provide the probability of exceedance of different limit states for a given hazard considering a range of magnitudes. In this paper, the vulnerability of slopes as expressed by a loss of performance is quantified for rainfall events of various intensities and duration. The approach expands upon probabilistic slope stability analysis and provides a rational logical framework for considering how vulnerable a slope is to rainfall-induced failure.

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.

The Irish Rail network was largely constructed in the mid-1800s. As a result of this, a significant proportion of the network is comprised of aged cuttings and embankments the construction of which predate modern design standards. Although most of the networks have remained stable for over a hundred and fifty years, a significant proportion of the network has slope angles in excess of those recommended in current design standards. Climate change predictions expect increased rainfall levels across Europe that will deteriorate these slopes further and increase incidence of failure. Current practice when populating earthwork asset databases is to conduct a technical walkover survey. Data obtained in this way is susceptible to bias errors and involve subjective approximations. This is particularly evident in slope geometry attributes such as slope height and angle. Remote sensing data is able to improve precision while reducing bias substantially, while being a much faster alternative than visual assessments on a network scale. Having precise and reliable data over the entire network is a fundamental prerequisite when conducting relative risk assessments of assets. In this paper, post-processed findings from an airborne LiDAR survey of the entire Irish Rail network are presented and compared to walkover assessment data. The current state of assets will also be discussed in light of modern design codes, together with the implications on infrastructure performance. Slope vulnerability to shallow planar type failures is expected to increase with predicted changes in climate such as increased environmental loading (rainfall events are predicted to be more intense and of longer duration, with longer dry periods in between). This type of failure is already the dominant failure mode across Irish Rail network. Typically these failures are instigated by rainwater percolating into the slope to a given depth, filling available pore space thus reducing in-situ soil suctions. This in turn reduces the shear strength of the soil. When the percolating water reaches some critical depth failure occurs. Fragility curves, a particular type of asset vulnerability assessment, provide a connection between triggering actions (such as rainfall) and expected damage to infrastructure assets. They can therefore be potentially useful in estimating a slope's response to predicted future climate loading. An example of a fragility curve applied to a typical slope on the Irish Rail network is presented in this paper.