This paper presents an overview of the theoretic framework of stochastic model updating,
including critical aspects of model parameterisation, sensitivity analysis, surrogate …

This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the
context of Bayesian model updating for engineering applications. Markov Chain Monte …

This paper gives an overview of recent advances in the field of non-probabilistic uncertainty
quantification. Both techniques for the forward propagation and inverse quantification of …

The consideration of imprecise probability in engineering analysis to account for missing,
vague or incomplete data in the description of model uncertainties is a fast-growing field of …

Quantitative analysis and optimal design under uncertainty are active research areas in
modern engineering structures and systems. A metamodel, as an effective mathematical …

In this paper we present a framework for addressing a variety of engineering design
challenges with limited empirical data and partial information. This framework includes …

This paper proposes a new non-probabilistic time-dependent reliability model for evaluating
the kinematic reliability of mechanisms when the input uncertainties are characterized by …

Structural reliability analysis for rare failure events in the presence of hybrid uncertainties is
a challenging task drawing increasing attentions in both academic and engineering fields …

Non-probabilistic convex models are powerful tools for structural model updating with
uncertain‑but-bounded parameters. However, existing non-probabilistic model updating …

Reliability-based optimization (RBO) offers the possibility of finding the best design for a
system according to a prescribed criterion while explicitly taking into account the effects of …