An approach is proposed for the evaluation of the probability density functions (PDFs) of the modal parameters for an ensemble of nominally identical structures when there is only access to a single structure and the dispersion parameter is known. The approach combines the Eigensystem realization algorithm on sets of dynamic data, with an explicit nonparametric probabilistic method. A single structure, either a mathematical model or a prototype, is used to obtain simulated data or measurements that are employed to build a discrete time state-space model description. The dispersion parameter is used to describe the uncertainty due to different sources such as the variability found in the population and the identification errors found in the noisy measurements from the experiments. With this approach, instead of propagating the uncertainties through the governing equations of the system, the distribution of the modal parameters of the whole ensemble is obtained by randomizing the matrices in the state-space model with an efficient procedure. The applicability of the approach is shown through the analysis of a two degrees-of-freedom mass-spring-damper system and a cantilever system. The results show that if the source of uncertainty is unknown and it is possible to specify an overall level of uncertainty, by having access to a single system's measurements, it is possible to evaluate the resulting PDFs on the modal parameters. It was also found that high values of the dispersion parameter may lead to nonphysical results such as negative damping ratios values.@en

Structural Health Monitoring uses data collected from sensors placed on structures to determine their operating condition and whether maintenance is required. Often, optimal sensor placement strategies are used to find the optimal locations for the identification of their modal properties, structural parameters and/or abnormal behaviours under the influence of model and measurement uncertainty. An approach that has been frequently used to solve the problem of sensor placement is the Bayesian experimental design. This approach chooses the locations using the data measured by the sensors to reduce the prior uncertainty of the parameters that are being inferred. The Bayesian experimental design minimizes the uncertainty of the parameters to be inferred through the use of metrics called utility functions. Most of these metrics are based on functions of the posterior distribution. In this paper, the use of three utility functions (Bayesian D-posterior precision, Bayesian A-posterior precision, and Expected Information Gain) is investigated for the problem of sensor placement. The case study chosen consists of a beam with translational and rotational springs connected to the ground subject to an impulsive load. The goal of the analysis is to select the most informative position of a sensor in order to update the distribution of two uncertain physical parameters of the beam based on natural frequencies extracted using the Eigensystem Realization Algorithm. It is shown that for the case investigated, the three utility functions yield the same optimal sensor location.@en