A probabilistic model of the economic risk to Britain’s railway network from bridge scour during floods

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system-wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests

Vocationalism varies (a lot): a 12-country multivariate analysis of participation in formal adult learning

To encourage adult participation in education and training, contemporary policy makers typically encourage education and training provision to have a strongly vocational (employment-related) character, while also stressing individuals’ responsibility for developing their own learning. Adults’ motivation to learn is not, however, purely vocational—it varies substantially, not only between individuals but between populations. This article uses regression analysis to explain motivation among 12,000 learners in formal education and training in 12 European countries. Although vocational motivation is influenced by individual-level characteristics (such as age, gender, education, occupation), it turns out that the country in which the participation takes place is a far stronger explanatory variable. For example, although men’s vocational motivation to participate is higher than women’s in all countries, Eastern European women have significantly higher levels of vocational motivation than men in Western Europe. This supports other research which suggests that, despite globalization, national institutional structures (social, economic) have continuing policy significance

A review of modelling methodologies for flood source area (FSA) identification

Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises

Risk-Based Approach for Bridge Scour Prediction: Applications for Design

NCHRP Project 24-34 was completed in September 2013 with the publication of NCHRP Report 761, "Reference Guide for Applying Risk and Reliability-Based Approaches for Bridge Scour Prediction." The project accomplished its objectives of developing risk/reliability-based methodologies that can be used in calculating bridge pier, abutment, contraction, and total scour at waterway crossings so that scour estimates can be linked to a probability. The developed probabilistic procedures are consistent with LRFD approaches for bridge design used by structural and geotechnical engineers. As a necessary first step in developing the statistical parameters for risk and reliability analyses, the key bridge scour equations (HEC-18 pier scour, Florida DOT pier scour, HEC-18 contraction scour, and NCHRP 24-20 abutment scour) were tested against available laboratory and field data sets. The results which are summarized in this presentation include: · The pier scour equations (HEC-18 and FDOT) are design equations which do not under predict observed scour very often. Consequently, the probabilistic reliability indexes for pier scour compare favorably with those used by structural and geotechnical engineers in LRFD applications for bridges. · In contrast with the pier scour equations, the HEC-18 contraction scour equations are essentially predictive, given that they are derived from sediment transport principles and theory. Therefore, under predictions of observed scour are much more common, and the resulting reliability is very low compared to typical target values used in LRFD applications. · The NCHRP 24-20 equations for live-bed and clear-water abutment scour both use a calculation for contraction scour and then apply an amplification factor to account for the additional scour caused by local effects at the tip of the abutment. The scour predicted by this method is the total scour at the abutment. The reliability of the abutment scour equations was found to be intermediate between those of the pier scour and contraction scour equations. This presentation will be followed by Part II which will provide an example application of the basic risk-based methodology in a typical design situation

Generating finite element models of the knee:How accurately can we determine ligament attachment sites from MRI scans?

In this study, we evaluated the intra- and inter-observer variability when determining the insertion and origin sites of knee ligaments on MRI scan images. We collected data of five observers with different backgrounds, who determined the ligament attachment sites in an MRI scan of a right knee of a 66-year-old male cadaver donor. We evaluated the intra- and inter-observer differences between the ligament attachment center points, and also determined the differences relative to a physical measurement performed on the same cadaver. The largest mean intra- and inter-observer differences were 4.30 mm (ACL origin) and 16.81 mm (superficial MCL insertion), respectively. Relative to the physical measurement, the largest intra- and inter-observer differences were 31.84 mm (superficial MCL insertion) and 23.39 mm (deep MCL insertion), respectively. The results indicate that, dependent on the location, a significant variation can occur when identifying the attachment site of the knee ligaments. This finding is of particular importance when creating computational models based on MRI data, as the variations in attachment sites may have a considerable effect on the biomechanical behavior of the human knee join