This paper proposes a model for assessing the risk posed by natural hazards to infrastructures, with a focus on the indirect losses and loss of stability for the population relying on the infrastructure. The model prescribes a three-level analysis with increasing level of detail, moving from qualitative to quantitative analysis. The focus is on a methodology for semi-quantitative analyses to be performed at the second level. The purpose of this type of analysis is to perform a screening of the scenarios of natural hazards threatening the infrastructures, identifying the most critical scenarios and investigating the need for further analyses (third level). The proposed semi-quantitative methodology considers the frequency of the natural hazard, different aspects of vulnerability, including the physical vulnerability of the infrastructure itself, and the societal dependency on the infrastructure. An indicator-based approach is applied, ranking the indicators on a relative scale according to pre-defined ranking criteria. The proposed indicators, which characterise conditions that influence the probability of an infrastructure malfunctioning caused by a natural event, are defined as (1) robustness and buffer capacity, (2) level of protection, (3) quality/level of maintenance and renewal, (4) adaptability and quality of operational procedures and (5) transparency/complexity/degree of coupling. Further indicators describe conditions influencing the socio-economic consequences of the infrastructure malfunctioning, such as (1) redundancy and/or substitution, (2) cascading effects and dependencies, (3) preparedness and (4) early warning, emergency response and measures. The aggregated risk estimate is a combination of the semi-quantitative vulnerability indicators, as well as quantitative estimates of the frequency of the natural hazard, the potential duration of the infrastructure malfunctioning (e.g. depending on the required restoration effort) and the number of users of the infrastructure. Case studies for two Norwegian municipalities are presented for demonstration purposes, where risk posed by adverse weather and natural hazards to primary road, water supply and power networks is assessed. The application examples show that the proposed model provides a useful tool for screening of potential undesirable events, contributing to a targeted reduction of the risk.

The paper presents an updated evaluation of the annual probability of failure for an embankment dam in Norway. Multiple plausible failure mechanisms were investigated. The first reliability analysis for the dam was performed in 1996. The purpose of the new reliability analysis for the dam was to update the analyses from 1996 with new knowledge from 2016, new methodologies and new competence on internal erosion; and to compare the dam reliability before and after the rehabilitation measures done between 1995 and 2016. The 1996 analyses were done with the event tree method, while the new reliability analyses were conducted with both the event tree and the Bayesian network approaches. After a screening of the plausible failure modes for the dam, the scenario occurrence probabilities and annual probability of dam breach were evaluated. The Bayesian network models were built based on the input from the event tree analysis. The Bayesian network analyses were supplemented with Monte Carlo simulations to obtain the statistics of the probability of dam breach in terms of the mean, high estimate and low estimate probabilities of failure. The Monte Carlo simulations used the probability bounds set by the experts participating in the event tree analysis. The results showed that the best estimate for annual probability of failure in 2016 for the embankment dam analyzed was 10^-5, which, based on the literature and ICOLD statistics, is significantly lower than the breach rate for embankment dams and other types of dams in the world. The new Bayesian network analyses provided a range of numbers for probability of failure including the mean and best estimate. This is useful information for the stakeholders in decision-making. The paper summarizes the strengths and weaknesses of the analyses done in 2016, and how the former (1996) and latter (2016) risk assessments add useful insights into the safety of a dam.

Our modern society relies on well-functioning transport systems providing mobility, transport safety and regularity. Maintaining the operational state of roads and railways during extreme weather events or other natural events is an important and demanding task. Natural events may cause damage to transportation assets, which can immediately or over time result in functional loss of a transportation line. For instance, a reduced culvert capacity due to debris deposition and clogging, could cause flooding of a road/rail. Some natural events can lead directly to loss of service, even without damaging an asset, like the occurrence of avalanches on a transportation line, blocking the related traffic. To reduce risks of failures posed by natural hazards, it is essential to assess vulnerability of transportation networks to such events.A well-established way to analyse vulnerability is to use damage-, loss- or fragility functions. Such functions can express both functional vulnerability, representing the functional loss for a transportation line, and structural vulnerability representing damage degree or the exceedance probability of damage levels pertinent to a transportation asset. These functions can all be expressed in terms of event intensity, which is a parameter characterizing the damaging potential of a natural event.In order to analyse functional vulnerability, various asset types with their interdependencies i.e. network topology and geographical coincidence must be considered. Here, the applied damage and fragility functions for evaluating structural vulnerability must account for location specific data on assets and asset properties. The review of existing damage-, loss- and fragility functions showed that these are not sufficient for intended analysis and need to be updated to consider various natural events and related failure modes. Recommendations are provided on how to elaborate new damage-, loss- and fragility functions to overcome a large number of uncertainties related to impacts of natural events on infrastructure and account for resistance of infrastructure. These recommendations concern both the choice of intensity parameters for different types of hazards and definition of possible failure modes, the methods for developing the functions and the assessment of the relationship between structural vulnerability of the asset and functional vulnerability.The research leading to these and future results receives funding from the European Community’s H2020 Programme MG-7-1-2017 Resilience to extreme (natural and man-made) events, under Grant Agreement number: 769255 - "GIS-based infrastructure management system for optimized response to extreme events of terrestrial transport networks (SAFEWAY)".

This paper proposes a model for assessing the risk posed by natural hazards to infrastructures. The model prescribes a three level analysis with increasing level of detail, moving from qualitative to quantitative analysis. The focus is on a methodology for semi-quantitative analysis to be performed at the second level. The purpose of this type of analysis is to perform a screening of the scenarios of natural hazards threatening the infrastructures, identifying the most critical scenarios and investigating the need for further analyses (third level). The proposed semi-quantitative methodology considers the frequency of the natural hazard, different aspects of vulnerability including the physical vulnerability of the infrastructure itself and the societal dependency on the infrastructure. An indicator-based approach is applied, ranking the indicators on a relative scale according to pre-defined ranking criteria. The proposed indicators, which characterize conditions that influence the probability of an infrastructure break-down caused by a natural event, are defined as 1) Robustness and buffer capacity, 2) Level of protection, 3) Quality/Level of maintenance and renewal, 4) Adaptability and quality in operational procedures and 5) Transparency/complexity/degree of coupling. Further indicators describe the societal consequences of the infrastructure failure, such as Redundancy and/or substitution, Restoration effort/duration, Preparedness, early warning and emergency response and Dependencies and cascading effects. The aggregated risk estimate is a combination of the semi-quantitative vulnerability indicators, as well as quantitative estimates of the frequency of the natural hazard, the potential duration of the infrastructure malfunctioning (depending e.g. on the required restoration effort) and the number of users of the infrastructure. Case studies for two Norwegian municipalities are presented where risk posed by adverse weather and natural hazards to primary road, water supply and power network is assessed. The application examples show that the proposed model provides a useful tool for screening of potential undesirable events, contributing to a targeted reduction of the risk.