Organic matter accretion, shallow subsidence, and river delta sustainability

Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures can entail critical conse...

Underground pipelines and railway infrastructure – failure consequences and restrictions

This paper presents a methodology tuned to support the management of underground pipelines (sewer and aqueduct networks) in Rome, often threatened by the sudden formation of sinkholes related to th...

Integration of satellite-based A-DInSAR and geological modeling supporting the prevention from anthropogenic sinkholes: a case study in the urban area of Rome

Using risk management systems for large-scale asset management is not without risk itself. Systems that collect measurement from a geographically diverse area, across many organisations, contain many interacting components that can fail in many different ways. In this chapter these systems are discussed from a risk assessment point of view, using practical examples. It provides suggestions how trust can propagate between interacting components of risk management systems by making information needed for risk assessment information explicit

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Practical Propagation of Trust in Risk Management Systems

Risk treatment is an effective way to reduce the risk of oil pipeline accidents. Many risk analysis and treatment strategies and models have been established based on the event tree method, bow-tie method, Bayesian network method, and other methods. Considering the characteristics of the current models, a risk treatment strategy model for oil pipeline accidents based on Bayesian decision network (BDNs) is proposed in this paper. First, the quantitative analysis method used in the Event-Evolution-Bayesian model (EEB model) is used for risk analysis. Second, the consequence weights and initial event likelihoods are added to the risk analysis model, and the integrated risk is obtained. Third, the risk treatment strategy model is established to achieve acceptable risk with optimal resources. The risk treatment options are added to the Bayesian network (BN) risk analysis model as the decision nodes and utility nodes. In this approach, the BN risk analysis model can be transformed into a risk treatment model based on BDNs. Compared to other models, this model can not only identify the risk factors comprehensively and illustrate the incident evolution process clearly, but also can support diverse risk treatment strategies for specific cases, such as to reduce the integrated risk to meet acceptable criterion or to balance the benefit and cost of an initiative. Furthermore, the risk treatment strategy can be updated as the risk context changes.

/pdf/a-risk-treatment-strategy-model-for-oil-pipeline-accidents-12g1zcb1.pdf

A Risk Treatment Strategy Model for Oil Pipeline Accidents Based on a Bayesian Decision Network Model

Pipeline networks for gas and water are the lifelines of our society. Most pipelines are buried, which obstructs direct monitoring of the pipes, and introduces the need for other techniques to prov...

Risk-based asset management: automated structural reliability assessment of geographically distributed pipeline networks for gas and water in the Netherlands

Due to heterogeneity of the subsurface in the delta environment of the Netherlands, differential subsidence over short distances results in tension and subsequent wear of subsurface infrastructure, such as water and gas pipelines. Due to uncertainties in the build-up of the subsurface, however, it is unknown where this problem is the most prominent. This is a problem for asset managers deciding when a pipeline needs replacement: damaged pipelines endanger security of supply and pose a significant threat to safety, yet premature replacement raises needless expenses. In both cases, costs – financial or other – are high. Therefore, an interdisciplinary research team of geotechnicians, geologists and Big Data engineers from research institutes TNO, Deltares and SkyGeo developed a stochastic model to predict differential subsidence and the probability of consequent pipeline failure on a (sub-)street level.

Willem Dabekaussen

Papers

Risk-based asset management: automated structural reliability assessment of geographically distributed pipeline networks for gas and water in the Netherlands

Differential subsidence and its effect on subsurface infrastructure: predicting probability of pipeline failure (STOOP project)

Application Theme 2 – Groundwater Evaluations