Novel Nonprobabilistic Bayesian Uncertainty Quantification Method for Structures with Interval Parameters

Structural reliability analysis is a crucial task in structural safety analysis. Owing to insufficient data information, traditional probabilistic methods do not accurately quantify input or output variables and the resultant assessment of structural reliability. Considering the development of engineering technology, a quantitative model of uncertainty based on improbability method is urgently needed. Herein, a novel nonprobabilistic Bayesian-inference-based interval uncertainty quantification model is developed with a user-specified credibility level. On the contrary, limited samples are introduced to update the bounds of the interval, and the conservativeness is decreased compared to the traditional unbiased interval estimation based on uniform distribution. Moreover, it can provide more conservative quantification than the extreme values-based interval model. Consequently, two examples are implemented to demonstrate the feasibility and effectiveness of the proposed model.