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 establishment of a Digital Twin of an operating engineered system can increase the potency of Structural Health Monitoring (SHM) tools, which are then bestowed with …
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 …
R Nayek, S Narasimhan - Journal of Civil Structural Health Monitoring, 2020 - Springer
Identification of bridge dynamic properties from moving vehicle responses presents several practical benefits. However, a problem that arises when working with vehicle responses for …
An optimal sensor placement (OSP) framework for virtual sensing using the augmented Kalman Filter (AKF) technique is presented based on information and utility theory. The …
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) …