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Abstract: In engineering decision making, we often face problems where the conditions governing certain response models vary spatially. In such cases, the use of hierarchical Bayesian models is often beneficial. [...]

The present paper describes the key steps and issues involved in performing a quantitative risk analysis (QRA) for a dynamically positioned (DP) offshore vessel that is used to perform a short-term production test (PT) in North Atlantic deep waters. The basic approach is to focus on the 'incremental' risk that would occur if the PT were run from a DP vessel as opposed to a fixed structure. The analysis is structured around two basic groups of risk: those specifically associated with DP vessel disconnection decisions and activities (all of which are seasonal), and those occurring during normal operation of the DP vessel. In the case of disconnection caused by hazards such as severe weather, ice, equipment or reference system malfunction, or human/operating error, a large variety of event sequences is assumed, each resulting in different consequences and risks. These are formulated for each analysis outcome in terms of loss of life, release of chemicals into the environment, and damage and loss of assets and equipment, as well as overall failure cost. It is shown that the QRA provides a very useful basis for optimal decision making with respect to the feasibility, the planning, and the risk/benefit of deep-water production testing from a DP vessel.

When performing a probabilistic assessment of the reliability of deteriorating structures, we often need to integrate the results of different inspections in time, within the models used to analyze the progress of deterioration. A new framework is described in this paper. It rests on a special case of the empirical Bayes method where the non-observable parameter is a discrete random variable with a relatively small number of outcomes. Various likelihood functions are derived. They are based on mixtures of deterioration scenarios. It is shown how the method can be used to calibrate the response of a stochastic deterioration model and to merge with a time-dependent reliability analysis. Examples relating to the long-term chloride corrosion in a reinforced concrete slab are presented in the paper. [DOI: 10.1115/1.1491973]

The present paper describes a quantitative analysis of the effect of model imprecision on reliability-based design rules. The presence of model uncertainty requires the insertion of additional safety elements in the corresponding design-check criteria. In this paper, this is considered to be achieved by the use of ignorance factors. A method is derived that allows these ignorance factors to be determined on the basis of just one structural reliability analysis performed without the inclusion of model uncertainty. This is based on a sensitivity analysis of uncertain parameters in a limit-state model, and on some basic considerations of the effect of expanding a perfect model to an imperfect one. This 'expansion' problem - the addition of random variables - is complementary to the well-known 'omission' problem, and, from this point of view, ignorance factors have a function similar to that of omission factors except that the ignorance factors can be used directly in design-check equations subject to model uncertainty. The present paper outlines the derivation of asymptotic expressions for expansion factors for both additive- and multiplicative-model uncertainty parameters. The basic inverse-reliability-problem format provides for the link between model expansion and ignorance factors. Various example applications show that the proposed user-friendly expressions for ignorance factors result in accurate and reliable design rules.

Objective. To determine the effectiveness of lumbar medial branch thermal radiofrequency neurotomy based on different selection criteria and procedural techniques. Design. Comprehensive systematic review. Methods. A comprehensive literature search was conducted, and all authors screened and evaluated the studies. The Grades of Recommendation, Assessment, Development, and Evaluation system was used to assess all eligible studies. Outcome Measures. The primary outcome measure assessed was the success rate of the procedure, defined by varying degrees of pain relief following neurotomy. Data are stratified by number of diagnostic blocks and degree of pain relief, as well as procedural technique with perpendicular or parallel placement of electrodes. Results. Results varied by selection criteria and procedural technique. At six months, 26% of patients selected via single medial branch block with 50% pain relief and treated via perpendicular technique achieved at least 50% pain relief; 49% of patients selected via dual medial branch blocks with 50% pain relief and treated via parallel technique achieved at least 50% pain relief. The most rigorous patient selection and technique—two diagnostic medial branch blocks with 100% pain relief and parallel electrode placement—resulted in 56% of patients experiencing 100% relief of pain at six months. Conclusions. This comprehensive systematic review found differences in the effectiveness of lumbar medial branch radiofrequency neurotomy when studies were stratified by patient selection criteria and procedural technique. The best outcomes are achieved when patients are selected based on high degrees of pain relief from dual medial branch blocks with a technique employing parallel electrode placement. [ABSTRACT FROM AUTHOR]

Byline: Marc A. Maes, Michael H. Faber The present paper focuses on the role and the modelling of preferences in risk based decision making for engineering systems, with special emphasis on the dislike of severe consequences. This involves the use of appropriate utility models and a proper understanding of the many aspects of risk perception. The basic premise of this paper is that the risk aversion intrinsic to nonlinear utility functions can almost always be explained by the non-inclusion of indirect and 'follow-up' consequences. Several aspects of risk perception and preference ranking can be interpreted as the result of the decision maker's voluntary or involuntary unwillingness to account for consequences that are triggered by extreme losses, such as excessive business losses, loss of reputation or other indirect or so-called intangible losses.