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 …

During the past decades, significant efforts have been dedicated to develop reliable
methods in structural health monitoring. The health assessment for the target structure of …

This paper exploits a new direction of structural identification by means of Neural Ordinary
Differential Equations (Neural ODEs), particularly constrained by domain knowledge, such …

Signal processing is the key component of any vibration-based structural health monitoring
(SHM). The goal of signal processing is to extract subtle changes in the vibration signals in …

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 …

A new probabilistic finite element (FE) model updating technique based on Hierarchical
Bayesian modeling is proposed for identification of civil structural systems under changing …

Due to the great challenges of measuring forces directly, identifying dynamic loads based on
accessible responses is a crucial problem in engineering, hel** ensure integrity and …

The difficulty of achieving controlled input has led to the development of new output-only
structural health monitoring (SHM) approaches. Without measuring the input or assuming a …

Accurate and efficient parameter identification along with uncertainty quantification in
nonlinear systems is crucial for enabling practical and reliable structural health monitoring …

The unscented Kalman filter (UKF) has proven to be an effective approach for the
identification of nonlinear systems from limited output measurements. However, the …