Modeling only constitutes one aspect of decision making. The prevailing limitation of
applying modeling to practice is the absence of explicit consideration of uncertainties. This …

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 …

The difficulty in quantifying the benefit of Structural Health Monitoring (SHM) for decision
support is one of the bottlenecks to an extensive adoption of SHM on real-world structures …

This article presents a brief survey on some of the latest developments in the area of
reliability-based design optimization of structural systems under stochastic excitation. The …

Monte Carlo methods have attracted constant and even increasing attention in structural
reliability analysis with a wide variety of developments seamlessly presented over decades …

Parameter identification using Bayesian approach with Markov Chain Monte Carlo (MCMC)
has been verified only for certain conventional simple constitutive models up to now. This …

In this paper, the sparse Bayesian learning (SBL) approach previously proposed for the
characterization of one-dimensional (1D) soil spatial variability is extended to a more …

This paper addresses the statistical uncertainties associated with the estimation of a depth-
dependent trend function and spatial variation about the trend function using limited site …

Bayesian inference is a popular approach towards parameter identification in engineering
problems. Such technique would involve iterative sampling methods which are often robust …

Stochastic approaches to structural health monitoring (SHM) are often inevitably limited by
computational constraints. For instance, for Markov chain Monte Carlo algorithms relying …