A practical framework for failure probability assessment under aleatory and epistemic uncertainty (REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World, 2026)
In engineering applications, aleatory and epistemic uncertainties commonly coexist and interact, making their proper characterization essential for reliability analysis and informed decision-making under uncertainty. This challenge becomes particularly evident when failure probabilities of engineering structures are evaluated using incomplete, insufficient, imperfect, or imprecise information. Under such circumstances, failure probability can no longer be expressed as a single deterministic value; instead, set-theoretic or Bayesian representations are required to explicitly account for epistemic uncertainty. Although the theoretical foundations for handling these uncertainties are well established, a noticeable gap persists between academic developments and practical engineering implementation. In practice, aleatory and epistemic uncertainties, despite being conceptually distinct, are still frequently mixed, either implicitly or even explicitly in engineering analyses. To bridge this gap, this paper offers a pragmatic guide for selecting suitable uncertainty modeling frameworks, such as probability boxes, fuzzy probability models, or hierarchical probabilistic approaches, when dealing with problems influenced by both uncertainty types. By assessing the nature and quality of available information as well as the objectives of the analysis, we provide clear recommendations for appropriate modeling choices and present a comprehensive framework for evaluating failure probability. Furthermore, this work highlights the critical role of sensitivity analysis in identifying influential parameters, enabling targeted data acquisition efforts that reduce epistemic uncertainty and ultimately enhance the credibility of reliability assessments.