Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …

Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …

Redundant design has been widely used in aerospace systems, nuclear systems, etc. which
calls for particular attention to common cause failure problems in such systems with various …

Due to the limitation of measured data, the random and epistemic uncertainties must be
simultaneously handled for the reliability analysis of curvilinear aircraft stiffened panels with …

Modern complex engineering systems oftentimes possess hierarchical structures which can
be physically divided into several levels. Traditional reliability assessment methods were …

In this paper, a new method for constructing the probability box (p-box) model is developed
based on maximum entropy principle. The distribution characteristics of probability box …

In this paper, we developed a machine learning-based Bayesian approach to inversely
quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) …

The probabilistic modelling is widely used in engineering practices, especially for assessing
the safety and reliability of complex systems. Dynamic evidential network (DEN) can …

In this work, the reliability of complex systems under consideration of imprecision is
addressed. By joining two methods coming from different fields, namely, structural reliability …

Importance measures of multi-state systems have been intensively investigated from
different perspectives in the past few years as the results are able to provide a valuable …