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

Nonlinear system identification is a vast research field, today attracting a great deal of
attention in the structural dynamics community. Ten years ago, an MSSP paper reviewing …

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 …

This paper presents a framework for structural health monitoring (SHM) and damage
identification of civil structures. This framework integrates advanced mechanics-based …

Finite element model updating is significant in damage detection, parameter identification
and structural control, yet practical applications to large-scale structure reveal a major defect …

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 …

Bayesian inference methods typically require a considerable amount of computation time in
the calculation of forward models. This limitation restricts the application of Bayesian …

A promising and attractive way of performing structural health monitoring (SHM) and
damage prognosis (DP) of engineering systems is through utilizing a nonlinear finite …

Bayesian finite element (FE) model updating using direct model evaluations of large-scale
high-fidelity FE models is extremely computationally expensive. Surrogate models can be …