Keeping transport links open in adverse conditions and being able to restore connections quickly after extreme events are important and demanding tasks for infrastructure owners/operators. This paper is developed within the H2020 project SAFEWAY, whose main goal is to increase the resilience of terrestrial transportation infrastructure. Risk-based approaches are excellent tools to aid in the decision-making process of planning maintenance and implementation of risk mitigation measures with the ultimate goal of reducing risk and increasing resilience. This paper presents a framework for quantitative risk assessment which guides an integrated assessment of the risk components: hazard, exposure, vulnerability and consequences of a malfunctioning transportation infrastructure. The paper guides the identification of failure modes for transportation infrastructure exposed to extreme events (natural and human-made) and provides models for and examples of hazard, vulnerability and risk assessment. Each assessment step must be made in coherence with the other risk components as an integral part of the risk assessment.

Estimation of the risk posed by inland fluvial floods to critical linear transportation infrastructures requires quantifying the damage inflicted by flooding on roads and railways. The estimated risk is often quantified in terms of the Expected Annual Damage [EAD] of the given linear structure. The aim of this article is to shed light on the estimation of this number and its uncertainty. The damage function models, which describe the degree of damage given flood intensity, represent a source of uncertainty. In this article we argue that this uncertainty needs to be embedded with a correlation structure in order to be evaluated for aggregated values. To this end, a novel methodology based on the use of Gaussian Processes to model the uncertainty associated with the damage function is introduced. Assuming a spatial correlation structure, parameterized by the so called decorrelation length scale, this framework is applied to estimate the EAD to roads in Portugal. The study shows that the application of an appropriate decorrelation length scale is decisive to the estimated uncertainty of the EAD. The proposed methodology may also be applicable to other types of hazards, if represented by some hazard intensity parameter associated with a damage function.

Open image in new window The effectiveness of landslide risk management should be assessed to optimize the implementation of landslide risk mitigation measures. The Risk Management Index (RMI) of Cardona et al. (Disaster risk and risk management benchmarking: a methodology based on indicators at national level. IDB/IDEA Program on Indicators for Disaster Risk Management, Universidad Nacional de Colombia, Manizales, p 101, 2004 ) provides useful procedures to holistically measure perceptions of risk management for natural hazards from selected actors. This paper uses Norway as a case study to present a modified RMI for surveying perceptions of landslide risk management at two time scenarios: 2015 (present) and 2050 (future), and for various administrative levels: national, county, and municipality. All survey respondents are practitioners in landslide risk management in Norway. The survey results are able to reflect some viewpoints of these experts on landslide risk management in Norway. Factors considered for assessing the future performance of landslide risk management by respondents are also studied to understand how respondents project their expectations. This paper also demonstrates how areas of improvement in landslide risk management in Norway can be identified based on the survey results. Due to limited responses, limited knowledge of respondents and the subjective nature of perceptions, the survey results are associated with uncertainties and should thus be used with care. Upon simplification of technical terms, the survey can be applied to survey public perceptions. The survey can also be regarded as a starting point for developing a common language/terminology for landslide risk management in Norway. This research activity has been funded by the Norwegian Centre of Innovation Klima 2050 ( www.klima2050.no ).

The reliability analyses of one rockfill embankment dam in Norway are presented. The event tree analysis and the Bayesian network approaches, the latter combined with Monte Carlo simulations, were used to obtain the annual probability of dam breach. The analyses were run partly in a workshop format, as a process to reach consensus among several experts on the parameters to consider in the calculation of probabilities. The analyses illustrate that the reliability analyses enabled the identification of an unexpected mode of failure and later quantified the effect of rehabilitation measures on the estimated probability of dam breach.