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 supervised learning scheme is proposed for detecting, locating, and quantifying the intensity
of damage in structures using Artificial Neural Networks (ANNs) and Proper Orthogonal …

In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et
al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and …

In this paper, we investigate the performance of reduced order modeling of dynamic structural
systems based on the proper orthogonal decomposition (POD) technique. Singular value …

Railway bridges are subjected to intermittent excitations induced by gravity loads. The
monitoring of stress time histories at fatigue prone areas (ie, “hot spots”) for steel, heavy haul …

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 degrees …

The effectiveness of Proper Orthogonal Decomposition (POD) for damage identification on
out of service, rural bridges is examined using field data. Three similar, out of service, simply …

This paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint
input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF), …

Dual estimation consists of tracking the whole state of partially observed systems, and
simultaneously estimating unknown model parameters. In case of nonlinearly evolving systems, …

A novel framework to accurately estimate nonlinear structural model parameters and unknown
external inputs (ie, loads) using sparse sensor networks is proposed and validated. The …