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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 rainfall-induced 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. [ABSTRACT FROM AUTHOR]

Soil classification is a means of grouping soils into categories according to a shared set of properties or characteristics that will exhibit similar engineering behaviour under loading. Correctly classifying site conditions is an important, costly, and time-consuming process which needs to be carried out at every building site prior to the commencement of construction or the design of foundation systems. This paper presents a means of automating classification for fine-grained soils, using a feed-forward ANN (Artificial Neural Networks) and CPT (Cone Penetration Test) measurements. Thus representing a significant saving of both time and money streamlining the construction process. 216 pairs of laboratory results and CPT tests were gathered from five locations across Northern Croatia and were used to train, test, and validate the ANN models. The resultant Neural Networks were saved and were subjected to a further external verification using CPT data from the Veliki vrh landslide. A test site, which the model had not previously been exposed to. The neural network approach proved extremely adept at predicting both ESCS (European Soil Classification System) and USCS (Unified Soil Classification System) soil classifications, correctly classifying almost 90% of soils. While the soils that were incorrectly classified were only partially misclassified. The model was compared to a previously published model, which was compiled using accepted industry standard soil parameter correlations and was shown to be a substantial improvement, in terms of correlation coefficient, absolute average error, and the accuracy of soil classification according to both USCS and ESCS guidelines. The study confirms the functional link between CPT results, the percentage of fine particles FC , the liquid limit w L and the plasticity index I P. As the training database grows in size, the approach should make soil classification cheaper, faster and less labour intensive. [ABSTRACT FROM AUTHOR]

Traditional slope stability analysis involves predicting the location of the critical slip surface for a given slope and computing a safety factor at that location. However, for some slopes with complicated stratigraphy several distinct critical slip surfaces can exist. Furthermore, the global minimum safety factor in some cases can be less important than potential failure zones when rehabilitating or reinforcing a slope. Existing search techniques used in slope stability analysis cannot find all areas of concern, but instead converge exclusively on the critical slip surface. This paper therefore proposes the use of a holistic multi modal optimisation technique which is able to locate and converge to multiple failure modes simultaneously. The search technique has been demonstrated on a number of benchmark examples using both deterministic and probabilistic analysis to find all possible failure mechanisms, and their respective factors of safety and reliability indices. The results from both the deterministic and probabilistic models show that the search technique is effective in locating the known critical slip surface while also establishing the locations of any other distinct critical slip surfaces within the slope. The approach is of particular relevance for investigating the stability of large slopes with complicated stratigraphy, as these slopes are likely to contain multiple failure mechanisms. [ABSTRACT FROM AUTHOR]

This study presents a probabilistic analyses of suction bucket installation in cohesionless soils. The spatial variability of soil properties is quantified using a representative survey dataset from practice. Vertical random field modelling is used to model cone resistance variability and probability density functions are fitted using drained parameter estimates. Conventional installation analysis methods are adapted for layered soil deposits. The effect of varying permeability is included through the incorporation of a finite element seepage model. Parametric uncertainties are considered through a Monte Carlo analysis and the results are interrogated through a variety of feasibility and sensitivity studies. Performing such an analysis allows a designer to gain insights into governing failure mechanisms and objectively quantify the impact of uncertainty regarding parameter estimates. • Integrating a seepage analysis improves installation design in layered soils. • Probability of unexpected failure reduces by incorporating parameter variability. • Quantitative reliability analyses provide insight on governing failure mechanisms. • Permeability variation has a large impact on suction bucket installation design. • Vertical random fields of the cone resistance require more extensive checks. [ABSTRACT FROM AUTHOR]

Worldwide, transport infrastructure is increasingly vulnerable to aging-induced deterioration and climate-related hazards. Often, inspection and maintenance costs far exceed the available resources, and numerous assets lack any rigorous structural evaluation. Space-borne synthetic aperture radar interferometry (InSAR) is a powerful remote sensing technology that can provide cheaper deformation measurements for bridges and other transport infrastructure with short revisit times, while scaling from the local to the global scale. As recent studies have shown InSAR accuracy to be comparable to that of traditional monitoring instruments, InSAR could offer a cost-effective tool for long-term, near-continuous deformation monitoring, with the possibility of supporting inspection planning and maintenance prioritisation while maximising functionality and increasing the resilience of infrastructure networks. However, despite the high potential of InSAR for structural monitoring, some important limitations need to be considered when applying it in practice. In this paper, the challenges of using InSAR for the purpose of structural monitoring are identified and discussed, with specific focus on bridges and transport networks. Examples are presented to illustrate the current practical limitations of InSAR, and possible solutions and promising research directions are identified. The aim of the paper is to motivate future action in this area and highlight the InSAR advances needed to overcome current challenges. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]