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 …

A particle filtering (PF) is a sequential Bayesian filtering method suitable for non-linear non-
Gaussian systems, which is widely used to estimate the states of parameters of interest that …

Particle filtering (PF) is a sequential Monte Carlo method that draws sample (particle) values
of state variables of interest to approximate the posterior probability distribution function …

Bayesian model updating provides a powerful framework for updating and uncertainty
quantification of models by making use of observations, following probability rules in the …

Development of digital twins is emerging rapidly in geotechnical engineering, and it often
requires real-time updating of numerical models (eg, finite element model) using multiple …

As a main task of inverse problem, model updating has received more and more attention in
the area of inspection, sensing, and monitoring technologies during the recent decades …

One of the most prominent goals of a structural health monitoring (SHM) system is to infer
the state of the structure to inform appropriate maintenance actions that affect performance …

An approach for the Bayesian model updating of a linear dynamic system using complex
modal data identified from dynamic test data is proposed in this paper. Very few works have …

Particle filters (PFs) estimate the true parameter state from samples of states, called
particles, that are drawn to construct an approximated posterior probability density function …

Bayesian updating of the reliability of deteriorating engineering structures based on
inspection data has been attracting a lot of attention recently because it can provide more …