A dual implementation of the Kalman filter is proposed for estimating the unknown input and
states of a linear state-space model by using sparse noisy acceleration measurements. The …

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 presents a novel nonlinear finite element (FE) model updating framework, in
which advanced nonlinear structural FE modeling and analysis techniques are used jointly …

Structural health monitoring (SHM) allows the acquisition of information on the structural
integrity of any mechanical system by processing data, measured through a set of sensors …

Finite element (FE) model updating has emerged as a powerful technique for structural
health monitoring and damage identification of civil structures. Updating mechanics-based …

In this paper, a framework is presented for the joint state tracking and parameter estimation
of partially observed structural systems characterized by a relatively large number of …

This paper introduces a novel method for real-time simultaneous input-state-parameter
estimation with modulated colored noise excitation. In this method, the unknown inputs are …

The present study was conducted in one of the major agriculture areas to check farmers
indigenous knowledge about the impacts of floods on their farming lives, food security …