The present work offers a comprehensive overview of methods related to condition
assessment of bridges through Structural Health Monitoring (SHM) procedures, with a …

The uncertainty afflicting modal parameter estimates stems from eg, the finite data length,
unknown, or partly measured inputs and the choice of the identification algorithm …

Abstract Model order selection is a fundamental task in subspace identification for estimation
of modal parameters, uncertainty propagation and damage diagnosis. However, the true …

Sparse Bayesian learning (SBL) has been proved to be an effective damage detection
strategy. In this research, a structural damage detection technique with two-stage modal …

Subspace-based damage detection methods are widely used for civil engineering structures
modeled as linear time-invariant systems. For operating wind turbines modeled as linear …

Modal identification of large-scale engineering structures may require multiple
measurements obtained from different locations, due to the limitations in the instrumentation …

Modal curvature ratio approach for the localization and quantification of damage in beam-
type structures is proposed, demonstrating capability across both dense and sparse …

Structural damage identification based on physical models is often transformed into an
optimization problem that minimizes the difference between measurement information of …

Modal parameter estimation from input/output data is a fundamental task in engineering. The
poly-reference least-squares complex frequency-domain (pLSCF) algorithm is a fast and …

The absence of excitation measurements may pose a huge challenge in the application of
many damage identification methods since it is difficult to acquire the external excitations …