Abstract
When assessing a reliability of building structures, probabilistic models are used that have some random values (RV) as their input. The statistical analysis of these RV inevitably introduces some uncertainties in the assessments of their parameters. In this paper a novel method is given to assess the probability of failure (PoF) of building structures (BS) in the conditions of epistemic uncertainty of the initial data. Epistemic uncertainty is a systemic uncertainty that emerges due to insufficient knowledge about the processes being studied and substitution of them by some approximation models. The uncertainty is accounted for using the Bayesian analysis.
Highlights
Design of building structures (BS) is conducted for the purpose of getting a guarantee that during their life cycle no limit state of any kind will occur
In the presented paper the limit state is related to the strength of the structure, which means that during the whole life cycle of the structure its bearing capacity has to be always larger than the loads and influences it is experiencing
In order to assess this type of uncertainty such methods as Bayesian analysis, fuzzy logic and the theory of proof, are used
Summary
Design of building structures (BS) is conducted for the purpose of getting a guarantee that during their life cycle no limit state (failure) of any kind will occur. The aleatory uncertainty, a.k.a. statistical uncertainty, is connected to the random character of the processes being studied. Aleatory uncertainty is the quintessence of randomness and reflects the unknown random results, which differ each time the same experiment is conducted. The quantitative assessment of the aleatory uncertainty can be relatively simple. In order to conduct this assessment, statistical modeling methods, like the Monte Carlo method [1,2], are used. The epistemic uncertainty, a.k.a. the systemic uncertainty is the result, in the first place, of the absence of sufficient knowledge about the processes and substitution of the needed knowledge by some approximate models.
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