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.

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Landslides cause hundreds of deaths and billions euros of damage to infrastructure and the environment each year. In order to predict the locations most susceptible to landslides, the field of landslide hazard assessment has gone through a massive development in the last twenty years by introducing a wealth of statistical and geotechnical landslide susceptibility models. However, these efforts have been largely restricted to landslides occurring in natural terrain even though landslides occurring on geotechnical assets on transportation networks can result in even greater consequences. Current risk assessment approaches for earthworks on large transportation networks still largely take form of subjective risk matrices with inputs gathered by visual walkover surveys using data stored in an asset database. This paper shows the application of two distinctive objective landslide susceptibility approaches on a case study of Irish rail. The first is a ‘statistical’, or ‘data-driven’ approach uses logistic regression as a statistical tool to establish the influence of slope-describing variables that have led to landslide occurrence. This approach draws the data from the asset database containing records of slope variables, and the adjoining landslide register. The same asset database is used as a basis for the second, ‘geotechnical’ or ‘deterministic’ approach. In this approach, geometrical and geotechnical properties of each slope are used to carry out probabilistic slope stability analysis, resulting in probability of failure for each slope. Both approaches result in susceptibility zoning for earthwork assets across the network, effectively ranking them in the criticality terms. This study compares the requirements, applicability and outcomes of each approach, and discuss the methods needed for developing each of them into hazard and risk assessments.@en

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.

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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.@en

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.

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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.

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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.

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A significant proportion of European rail networks are built upon earthworks that are over one hundred years old. These earthworks are under increased pressure as they have to contend with heavier and more frequent traffic, far outside the scope of their design. To compound this problem further, recent years have seen unpredictable weather patterns develop with prolonged intense rainstorms commonplace. This has led to increased incidence of slope failures along rail networks, as many aged earthworks struggle to withstand such drastic changes in loading. Marginal engineered slopes fail depending on the triggering mechanism which presents itself first. Therefore the failure surface is intrinsically linked to the applied load i.e. surcharge loading will instigate a different type of landslide than prolonged rainfall. Therefore this paper proposes to analyse marginal slopes probabilistically as a system, where multiple slip circles are considered. A multi-modal optimisation algorithm LIPS (locally informed particle swarm optimisation) is used to locate all significant slip circles. In a slope with multiple potential failure surfaces the consequence of failure is not necessarily the same across the different slip surfaces. This paper addresses this deficit by examining the consequence of the different landslides should they occur. When combined with previously calculated probabilities of failure this will entail amount to a full geotechnical risk assessment of engineered slopes.@en

Earthworks such as cuttings and embankments account for a major part of the entire transport network infrastructure. Large parts of that infrastructure in Europe are susceptible to a range of geohazards, landslides being the most prevalent. These landslides frequently result in direct damage to assets, deaths and injuries, while indirectly also leading to traffic disruptions. There is a need therefore to identify critical assets where remediation efforts should be prioritised in order to prevent such events from occurring. Current state of the art practice involves using qualitative risk matrices, where the hazard and consequence components are determined through subjective visual survey observations. Landslide hazard analysis determines the spatial (susceptibility) and temporal probability of landslides of a certain intensity occurring over an observed area. A number of quantitative methods for landslide hazard and risk assessment have been developed recently; generally these methods are considered more effective due to their reduced subjectivity and their consideration of additional factors. A number of studies outline the application of these methods to natural terrain, but to date these methods have not been developed for transport network earthworks. This study presents and compares the results of two landslide susceptibility analysis approaches for cuttings and embankments on a section of Irish Rail network. The first, “geotechnical” approach uses probabilistic slope stability calculations to rank the assets by their reliability index. The second, “statistical” or “data driven” approach, uses logistical regression as a statistical tool to obtain the susceptibility ranking of the earthworks, using the database of previous failures on the network as an input. Furthermore, several methods for obtaining the temporal hazard characteristics are presented and applied, these methodologies combine to provide a full hazard assessment map of the network.@en