Abstract: The dynamic response of a general class of continuous linear vibrating systems is analyzed which possess damping properties close to those resulting in classical (uncoupled) normal modes. First, ...

Abstract: A two-step probabilistic structural health monitoring approach is used to analyze the Phase II experimental benchmark studies sponsored by the IASC–ASCE Task Group on Structural Health Monitoring. This study involves damage ...

Abstract: A new approximate asymptotic method is presented for the nonstationary response of classically damped MDOF (multidegree‐of‐freedom) linear systems to a general class of nonstationary stochastic excitations. The method is ...

Abstract: The problem of updating a structural model and its associated uncertainties by utilizing dynamic response data is addressed using a Bayesian statistical framework that can handle the inherent ill-conditioning and possible ...

Abstract: The problem of model identifiability is addressed. The goal is to find all the structural models within a specified class that produce the same output at a set of observed degrees of freedom as a given model in the class ...

Abstract: A method is presented for efficiently computing small failure probabilities encountered in seismic risk problems involving dynamic analysis. It is based on a procedure recently developed by the writers called Subset ...

Abstract: The seismic risk assessment of a structure in performance-based design (PBD) may be significantly affected by the representation of ground motion uncertainty. In PBD, the uncertainty in the ground motion is often represented ...

Abstract: An asymptotic approximation is developed for evaluating the probability integrals that arise in the determination of the reliability and response moments of uncertain dynamic systems subject to stochastic excitation. The ...

Abstract: A Bayesian probabilistic methodology for structural health monitoring is presented. The method uses a sequence of identified modal parameter data sets to compute the probability that continually updated model stiffness ...

Abstract: In Bayesian model updating, parameter identification of structural systems using modal data can be based on the formulation of the likelihood function as a product of two probability density functions, one relating to modal ...