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 …

The use of heterogeneous, non‐collocated measurements for nonlinear structural system
identification is explored herein. In particular, this paper considers the example of sensor …

An algorithm is presented for jointly estimating the input and state of a structure from a
limited number of acceleration measurements. The algorithm extends an existing joint input …

Data-driven approaches to structural health monitoring (SHM) have been recently shown to
be a powerful paradigm, helping to lead to an evolution of traditional scheduled-based …

Within a structural health monitoring (SHM) framework, we propose a simulation-based
classification strategy to move towards online damage localization. The procedure combines …

Recent advances in learning systems and sensor technology have enabled powerful
strategies for autonomous data-driven damage detection in structural systems. This work …

In many practical structural applications, unknown states, inputs and parameters are
present. However, most methods require one or more of these variables to be known in …

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 …

This paper proposes a novel framework that combines high-fidelity mechanics-based
nonlinear (hysteretic) finite-element (FE) models and a nonlinear stochastic filtering …

Joint estimation of unknown model parameters and unobserved state components for
stochastic, nonlinear dynamic systems is customarily pursued via the extended Kalman filter …