No abstract is provided for this article.
The EC research project ”DETAILS” aims to remove uncertainties on dynamic interaction effects of composite railway bridges. As part of the experimental part of the project, the Sesia viaduct, located on the new Italian high-speed line between Torino and Milano, has been tested under the excitation of Italian ETR500Y high-speed trains. This paper presents the finite element modeling of the Sesia viaduct, as well as the train-bridge interaction model and its implementation. To facilitate the calculation, a user friendly train-bridge interaction toolbox DATIS has been developed in Matlab that communicates with a finite element program. The toolbox is validated by comparing calculated accelerations and strains with measured results.
No abstract is provided for this article.
Vibration-based Structural Health Monitoring is a non-destructive condition assessment method that exploits damage-related changes in the dynamic characteristics of a structure. The sensitivity of some commonly employed dynamic characteristics such as natural frequencies, to local damage of moderate severity might be lower than their sensitivity to environmental influences such as temperature. Modal strains are more sensitive to local damage, but the accurate dynamic monitoring of the very low strain levels that occur in civil engineering structures under ambient excitation became possible only recently. In this work, the influence of both temperature and damage on modal strains is experimentally investigated for a prestressed concreted beam in controlled laboratory conditions. Not only the modal strains themselves are considered, but also the neutral axis position under bending deformation, which relates directly to the bending stiffness. It is found that the induced temperature changes in the concrete beam do not have a measurable influence on the modal strains and neutral axis positions. The cracks that are induced in the beam by external loading in a progressive damage test do have a clear and local influence on the strain mode shapes and neutral axis positions, even at low damage levels.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Built-up vibro-acoustic systems may contain components whose vibration field is sensitive to uncertainty from small spatial variations in geometry, material properties, or boundary conditions, which have a wave scattering effect. Such components can be modeled as diffuse. Statistical energy analysis (SEA) and related approaches are frequently employed for analyzing built-up systems with diffuse components, but their scope is limited because subsystems must be weakly coupled, the displacement fields are not modeled but only the total energies, the joint response probability density function is not available, and the computational efficiency decreases when there is additional parametric uncertainty. Recently, a method of analysis was presented that overcomes those limitations. It is a computationally efficient Monte Carlo method in which the subsystem displacement fields rather than the total energies are modeled. The method relies on the fact that the undamped eigenvalues of a diffuse wave field conform to those of a Gaussian Orthogonal Ensemble (GOE) matrix, while the mode shapes are Gaussian random fields. In this paper, the method is generalized such that diffuse components that are not homogeneous and isotropic, and that have a non-constant modal density, can be analyzed. This is achieved by transforming the GOE eigenvalue spacings to conform to the modal density of a diffuse component, and by relating its mode shape correlation function to the Green’s function of the corresponding unbouded subsystem. Parametric uncertainty is included in a straightforward way. The accuracy and computational efficiency of this field-based method is investigated by comparison with the energy-based (hybrid deterministic-)SEA method and with a detailed Monte Carlo method, where the diffuse field assumption is not made and local random wave scatterers are explicitly modeled. It is found that the new approach is more accuracte than SEA and also more computationally efficient when there are several or many uncertain parameters.
Locally resonant metamaterials are employed to increase the sound insulation of a host structure. In orthotropic host structures, the direction-dependent bending stiffness leads to a broad coincidence dip in sound transmission loss, which is difficult to overcome through conventional metamaterial solutions based on single-mode translational resonators. For this reason, in this paper we aim at developing orthotropic metamaterials with rotational resonators, exhibiting a direction-dependent effectiveness, and with multimodal resonators, which combine translational and rotational modes and are effective in a broader frequency band. First, we develop an analytical effective medium model of sound transmission in multimodal orthotropic metamaterials, which exhibit similar accuracy to simulations exploiting a wave and finite element method. We then propose a design methodology for realizable geometrical layouts of multimodal resonators, by exploiting numerical optimization to maximize the broadband sound transmission loss of the metamaterial. For two selected design cases, we show that rotational and multimodal metamaterials can suppress the broad coincidence dip of orthotropic plates, when the frequencies of local resonances are tuned within the coincidence region. The results of the work open up new possibilities for broadband sound insulation through metamaterial solutions.
This paper presents a joint input-state estimation algorithm that can be used for the identification of forces applied to a structure and for the extrapolation of the measured data to unmeasured response quantities of interest. The estimation of the input and the system states is performed in a minimum-variance unbiased way, based on a limited number of response measurements and a system model. No prior information on the system input is required, permitting online application of the algorithm. The paper also presents a novel approach for quantification of the estimation uncertainty originating from measurement errors and unknown stochastic excitation, that is acting on the structure besides the forces that are to be identified. The joint input-state estimation algorithm and the uncertainty quantification approach are verified using numerical simulations.
