Structural damage identification has received considerable attention during the past
decades. Although several reviews have been presented, some new developments have …

Bayesian inference provides a powerful approach to system identification and damage
assessment for structures. The application of Bayesian method is motivated by the fact that …

This paper presents two adaptive Kalman filters (KFs) for nonlinear model updating where,
in addition to nonlinear model parameters, the covariance matrix of measurement noise is …

This paper presents a recursive Bayesian inference framework for digital twinning of an
offshore wind turbine (OWT) in the Block Island Wind Farm (BIWF), the first commercial …

A successive Bayesian filtering framework for addressing the joint input-state-parameter
estimation problem is proposed in this study. Following the notion of analytical, rather than …

This paper proposes a novel approach to deal with modeling uncertainty when updating
mechanics-based nonlinear finite element (FE) models. In this framework, a dual adaptive …

A supervised learning scheme is proposed for detecting, locating, and quantifying the
intensity of damage in structures using Artificial Neural Networks (ANNs) and Proper …

Identification of bridge dynamic properties from moving vehicle responses presents several
practical benefits. However, a problem that arises when working with vehicle responses for …

Track irregularities affect the running safety of railway vehicles and ride comfort, hence track
irregularity identification using the dynamic responses of in-service vehicles is of great …

The quantification of the uncertainty effect of random system parameters, such as the
loading conditions, material and geometric properties, on the system output response has …