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Robustness, resilience, and anticipation are essential aspects of a system’s first line of defence against exceptional hazards and high-impact unexpected events. However, design and operational envelopes seem to be ever widening due to the expectation to consider increasingly unlikely and extreme scenarios, including some that can be termed nightmare scenarios. This paper questions both the principles and the need of digging too deep into the often intangible world of high-consequence low-probability events that have the potential to impact systems and society as a whole. The engineering community has recently taken to referring to certain events as black swans, perfect storms, or events caused by a priori unknowable unknowns, as if such appellation would constitute an apology for their sudden occurrence. We show that many of these metaphoric notions are either conceptually inappropriate, mostly from an epistemic point of view; or else, that they have little operational bearing on our knowledge base and on best-practice rational decision making. While extremes-beyond-extremes speak to the imagination and may spur creative analysis and research, they carry in them the elements of fear and surprise, neither of which brings good counsel to effective risk management. Worse, freakish events are often used as an excuse for failing to act proactively. Conversely, it is a common misgiving to think that if we prepare enough for the worst of the worst, it won’t happen. The origins, the classification, and the characteristics of exceptional events such as colored swans, perfectly aligned fail-sequences, freak accidents, and unimaginable scenarios, are discussed. Some case studies illustrate their conceptual and metaphorical role in a world of interconnected networks of everything. We show that the basic framework of decision making is broad enough to include challenging socio-economic and environmental consequences, exceptional hazards, and optimal strategic planning, so that provisions with respect to robustness, resilience, anticipation, and risk mitigative measures in general, can effectively be accounted for.

Corrosion in pipelines can lead to leak and rupture failures with significant consequences for people, the economy, and the environment. Corrosion growth rates are often determined from the results of in-line inspections. Detected corrosion anomalies from at least two inspections are matched with respect to their location in the pipeline and the measured growth path is used to infer current and future corrosion growth. Reliable defect matching is essential to infer credible corrosion rates for pipeline integrity assessments. If high-density corrosion is present, as frequently observed in upstream and subsea pipelines, reliable matching can typically be performed for a limited number of features. The remaining unmatched elements are usually removed from the corrosion growth analysis. The objective of this paper is to introduce a population-based corrosion growth analysis that neither requires matched defects nor removes corrosion anomalies from the analysis. All reported corrosion anomalies from an inspection are considered as a single population and the probabilistic approach determines the corrosion growth of two given populations. Adjustments are included for sizing, detectability, and false call uncertainties to determine the underlying true corrosion growth process. The results are used to estimate future corrosion evolution in joints and entire pipeline segments for informed deterministic or reliability-based integrity assessments of pipelines. The major advantage of this new approach is that the model can easily process mass pipeline inspection data.

In a rather well publicized June 2013 CBC broadcast, the safety of a specific series of Canadian onshore gas pipeline joints was declared “absolute” by one proponent, since, historically, no incidents or failures had ever been reported for that site. An opponent then argued that the face value of this risk could never be zero, but “very small”. The objective of the present paper is to review just how small, and at which confidence level, one can sensibly consider the actual incidence rate to be. A comparison of the most popular approaches and a comprehensive test for consistency, point to the superiority of the Bayesian estimator together with a non-central posterior probability interval.

International audience; BACKGROUND:The long-term safety of growth hormone treatment is uncertain. Raised risks of death and certain cancers have been reported inconsistently, based on limited data or short-term follow-up by pharmaceutical companies.PATIENTS AND METHODS:The SAGhE (Safety and Appropriateness of Growth Hormone Treatments in Europe) study assembled cohorts of patients treated in childhood with recombinant human growth hormone (r-hGH) in 8 European countries since the first use of this treatment in 1984 and followed them for cause-specific mortality and cancer incidence. Expected rates were obtained from national and local general population data. The cohort consisted of 24,232 patients, most commonly treated for isolated growth failure (53%), Turner syndrome (13%) and growth hormone deficiency linked to neoplasia (12%). This paper describes in detail the study design, methods and data collection and discusses the strengths, biases and weaknesses consequent on this.CONCLUSION:The SAGhE cohort is the largest and longest follow-up cohort study of growth hormone-treated patients with follow-up and analysis independent of industry. It forms a major resource for investigating cancer and mortality risks in r-hGH patients. The interpretation of SAGhE results, however, will need to take account of the methods of cohort assembly and follow-up in each country.