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Determination of the relevant soil stratigraphy is of the paramount importance for any geotechnical analysis. The cone penetration test (CPT) is the cost-effective, rapid, continuous, and reliable testing method for assessing soil layering and estimating in-situ mechanical properties of soil, and as such is especially useful for subsoil investigations along linear infrastructure networks, such as roads, highways, or railways. The design soil profile can be effectively determined using the CPT-based soil behaviour type (SBT) classification system. However, the soil profile consists of layers of various thickness and some layers can be only a few centimetres thick. Because the cone needs to penetrate to a certain depth to develop the cone resistance and to identify the presence of another layer, thin layers of soil cannot be properly detected. The soil layering algorithm, presented in this paper, merges these thin layers into the adjacent layers and thus overcomes the unreliable determination of the thin layers. The implementation of the proposed algorithm is demonstrated using a CPT carried out on the embankment test-site in north Croatia.

Knowledge of the fines content is necessary for all soil classification systems and an important factor in the evaluation of soil strength in liquefaction and seismic settlement analysis. This paper presents the application of cone penetration test, CPT data for estimating the soil fines content. The correlation can be used either as a first estimate of fines content (for example in the offshore environment) or to provide statistical information on the variation of fines content within a given area of interest (e.g. for a regional liquefaction study). The paper shows how field and laboratory test data were used with a neural network to correlate the CPT results and the fines content. Data from five site investigation locations across Northern Croatia were utilised. Verification of the approach is performed using field and lab test data from the Veliki vrh landslide.

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.

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.

Many railway embankments across Europe were constructed over 150 years ago. These embankments were not subject to rigorous design practice but instead were crudely constructed using end tipping techniques. As a result, the majority of these embankments are overly steep and far in excess of the design angles recommended in Eurocode 7. Over recent years, increased incidence of failure has been witnessed on these slopes following periods of prolonged or intense precipitation. This paper develops fragility curves to investigate how sensitive these steep slopes are to shallow translational failure when subjected to prolonged or abnormally intense rainfall. Rainfall intensity and condition are both considered for a range of slope angles. The significance of the findings are discussed in the context of transport slope asset management and risk assessment. The approach is a logical expansion on probabilistic slope stability analysis and could be used to interpret how vulnerable the transport network is to changing climatic condition.