Environmental variability is still a major challenge in structural health monitoring. Due to the
similarity of changes caused by environmental variations to damage, false positive and false …

The hierarchical Bayesian modeling (HBM) framework has recently been developed to
tackle the uncertainty quantification and propagation in structural dynamics inverse …

A Bayesian dynamic linear model (BDLM) framework for data modeling and forecasting is
proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting …

We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads
(DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads …

A general framework for modeling dependence in multivariate time series is presented. Its
fundamental approach relies on decomposing each signal inside a system into various …

This paper introduces a novel deep learning model that integrates a variational autoencoder
(VAE) and a neural ordinary differential equation (ODE) for detecting abnormalities in wind …

Abstract Vibration-based Structural Health Monitoring (VSHM) is becoming one of the most
commonly used methods for damage diagnosis and long term monitoring. In data-driven …

This study introduces a novel Bayesian framework for online and real-time vibration control
of beam type structures, which represent a comprehensive control system associated with …

Modal properties are recognized as indicators reflecting structural condition in structural
health monitoring (SHM). However, changing environmental and operational variables …

The representation of structural dynamics in the absence of physics-based models, is often
accomplished through the identification of parametric models, such as the autoregressive …