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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. [ABSTRACT FROM AUTHOR]

Earthworks such as embankments and cuttings are integral to road and rail networks but can be prone to instability, necessitating rigorous and continual monitoring. To date, the potential of remote sensing for earthwork hazard assessment has been largely overlooked. However, techniques such as airborne laser scanning (ALS) are now ripe for addressing these challenges. This research presents the development of a novel hazard assessment strategy, combining high-resolution remote sensing with a numerical modeling approach. The research was implemented at a railway test site located in northern England, U.K.; ALS data and multispectral aerial imagery facilitated the determination of key slope stability variables, which were then used to parameterize a coupled hydrological-geotechnical model, in order to simulate slope behavior under current and future climates. A software toolset was developed to integrate the core elements of the methodology and determine resultant slope failure hazard which could then be mapped and queried within a geographical information system environment. Results indicate that the earthworks are largely stable, which is in broad agreement with the management company's slope hazard grading data, and in terms of morphological analysis, the remote methodology was able to correctly identify 99% of earthworks classed as embankments and 100% of cuttings. The developed approach provides an effective and practicable method for remotely quantifying slope failure hazard at fine spatial scales (0.5 m) and for prioritizing and reducing on-site inspection. [ABSTRACT FROM AUTHOR]

Abstract: The South Korean-engineered slope database consists of 6828 slope observations. In this study, general slope factors were analyzed and classified by using a decision tree algorithm to evaluate the validity of the Korean slope database. The decision tree technique was used to automatically extract significant rules from a massive amount of data. These rules can be used to predict the failure possibility of new slopes. In order to rank the importance of the slope factors in the database, attribute evaluators such as ReliefF, Information Gain, Gain Ratio, Symmetrical Uncertainty and Chi square were used. These are independent computer algorithms that can automatically assess the importance of individual attributes. The most relevant attributes to slope failure, in order of importance, are fracture orientation, lithology, presence of seepage, rainfall, engineered slope angle, notches, tectonic domain, topography and weathering. Natural slope angle, height and length were found to be less important. The output of the decision tree consisted of 174 slope rules. The prediction rate of the decision tree was 72%. These rules could be grouped into two types. The first type suggested that failure is likely to occur when seepage is present, when slope angle is higher than 61°, when precipitation amount is high. These are commonly accepted characteristics of a slope failure, and such rules obtained from the constructed decision tree from Korean highway data suggested that the analyzed data and the methodology were statistically valid. The other type suggested that slope failure is highly dependent on the interaction of various slope factors. As an example, slopes with granitic gneiss failed when rainfall amount was high and did not fail, when low. However, this safe slope could fail at high weathering. In order to strengthen the validity of these rules, it should be applied to a new set of engineered slopes. [Copyright &y& Elsevier]