In this paper, a novel dynamic response reconstruction method based on multi-rate Kalman
filtering (MRKF) is presented. The proposed method starts with representing the structural …

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 …

This study proposes a novel filtering method for unbiased minimum variance state
estimation. A key challenge in the development of digital twinning and system identification …

The health monitoring of soft-story is vital during earthquakes. Inter-story drift can reflect the
structural damage state and provide evidence for assessment. However, installing …

Recursive Bayesian filters have been widely deployed in structural system identification
where output-only filters are of higher practicality. Unfortunately, the estimation obtained by …

The capability of a novel Kullback–Leibler divergence method is examined herein within the
Kalman filter framework to select the input–parameter–state estimation execution with the …

Observed data from a dynamic system can be assimilated into a predictive model by means
of Kalman filters. Nonlinear extensions of the Kalman filter, such as the Extended Kalman …

Although the extended Kalman filter (EKF) provides a promising way for structural state
estimation, it cannot effectively track time-varying parameters online. Besides, the structural …

A digital twin (DT) is a model that mirrors a physical system and is continuously updated with
real-time data from the physical system. Recent implementations of reduced-order-model …

In this paper, the problem of moving load identification using pure displacement
measurements is addressed. It is known that when no assumptions are made on the …