IGNORANCE FACTORS USING MODEL EXPANSION

The present paper describes a quantitative analysis of the effect of m odel imprecision on reliability-based design rules. The presence of mo del uncertainty requires the insertion of additional safety elements i n the corresponding design-check criteria. In this paper, this is cons idered to be achieved by the use of ignorance factors. A method is der ived that allows these ignorance factors to be determined on the basis of just one structural reliability analysis performed without the inc lusion of model uncertainty. This is based on a sensitivity analysis o f uncertain parameters in a limit-state model, and on some basic consi derations of the effect of expanding a perfect model to an imperfect o ne. This ''expansion'' problem-the addition of random variables-is com plementary to the well-known ''omission'' problem, and, from this poin t of view, ignorance factors have a function similar to that of omissi on factors except that the ignorance factors can be used directly in d esign-check equations subject to model uncertainty. The present paper outlines the derivation of asymptotic expressions for expansion factor s for both additive- and multiplicative-model uncertainty parameters. The basic inverse-reliability-problem format provides for the link bet ween model expansion and ignorance factors. Various example applicatio ns show that the proposed user-friendly expressions for ignorance fact ors result in accurate and reliable design rules.