In this article, an overview of Bayesian methods for sequential simulation from posterior
distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is …

The input-parameter-state estimation capabilities of a novel unscented Kalman filter is
examined herein on both linear and nonlinear systems. The unknown input is estimated in …

The use of machine learning in structural health monitoring is becoming more common, as
many of the inherent tasks (such as regression and classification) in developing condition …

This contribution presents a hierarchical Bayesian filter for recursive input, state and
parameter estimation using spatially incomplete and noisy output-only vibration …

The problem of identifying dynamic structural systems is of key interest to modern
engineering practice and is often a first step in an analysis chain, such as validation of …

This paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint
input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF) …

Structural identification and damage detection can be generalized as the simultaneous
estimation of input forces, physical parameters, and dynamical states. Although Kalman-type …

Rapid development of offshore wind foundation models has resulted in a large number of
built structures with generally underestimated foundation stiffness properties and a need to …

A novel framework to accurately estimate nonlinear structural model parameters and
unknown external inputs (ie, loads) using sparse sensor networks is proposed and …

The dynamic structural load identification capabilities of the gated recurrent unit, long short-
term memory, and convolutional neural networks are examined herein. The examination is …