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No abstract is provided for this article.
The problem of vehicle–bridge dynamic interaction system under articulated high speed trains is studied in this paper. A dynamic interaction model of the bridge-articulated train system is established, which is composed of an articulated vehicle element model and a finite element bridge model. The vehicle model is established according to the structure and suspending properties of the articulated vehicles. A computer simulation program is worked out. As an example, the case of the Thalys articulated train passing along the Antoing Bridge on the Paris–Brussels high speed railway line is analyzed. The dynamic responses of the bridge and the vehicles are calculated. The proposed analysis model and the solution method are verified through the comparison between the calculated results and the in situ measured data. The vibration behaviour of the articulated trains is discussed.
This chapter focuses on a seismic monitoring experiment conducted on the bell tower of the San Frediano Church in Lucca, Italy. The tower, dating back to the 11th century, has been fitted along its height with four triaxial seismometric stations which were then left active on the tower for four days. Ambient vibration monitoring provides important information on the structural health of ancient masonry constructions, as it is a non-destructive technique able to capture the most important features of their dynamic behaviour, such as natural frequencies, mode shapes and wave propagation velocities. The chapter presents the results of a seismic monitoring experiment conducted on the bell tower of the Basilica of San Frediano in Lucca, Italy. The tower has been instrumented with four highsensitivity triaxial seismometric stations, left active on the tower from 29 May to 3 June, 2015. This sophisticated instrumentation, usually employed in seismic monitoring networks, had already been installed on the Asinelli and Garisenda towers in Bologna.
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This paper presents a technique for offline time synchronization of data acquisition systems for linear structures with proportional damping. The technique can be applied when direct synchronization of data acquisition systems is impossible or not sufficiently accurate. The synchronization is based on the acquired dynamic response of the structure only, and does not require the acquisition of a shared sensor signal or a trigger signal. The time delay is identified from the spurious phase shift of the mode shape components that are obtained from system identification. A demonstration for a laboratory experiment on a cantilever steel beam shows that the proposed methodology can be used for accurate time synchronization, resulting in a significant improvement of the accuracy of the identified mode shapes.
{Fibre-optic Bragg Grating (FBG) strain sensors hold a great potential for vibration monitoring of civil structures because of their exceptional stability and accuracy; however, the accurate measurement of the very small strains levels occurring during ambient, or operational, excitation has been so far problematic. There are two ways to improve the measurement resolution: employing a strain-enhancing sensor package, and applying an improved wavelength detection algorithm. In this work, the potential of an improved wavelength detection algorithm for the identification of modal characteristics based on sub-microstrain data is investigated. The strategy is illustrated for a steel beam to which a chain of multiplexed FBG sensors has been attached at the top side of the beam. The raw FBG data are processed into strain values with an algorithm that is based on detecting the peak shifts in the wavelength spectrum by correlation analysis rather than by simply tracking the peak values. Subsequently, the strain sequences are used for identification of modal characteristics of the beam (natural frequencies and strain mode shapes) with ``Covariance driven Stochastic Subspace Identification (SSI/cov){''}. Computational results of a Finite Element Model are used to validate the experimental results.}
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An attractive feature of FRP structures is that Fiber-Optic Sensors (FOS) can be naturally embedded during the production process and provide important structural information immediately after construction. Thus, FOS became very useful also for Vibration-Based Monitoring (VBM) applications. In VBM, damage can be identified by detecting damage-related changes in the modal characteristics of a structure, such as natural frequencies and strain mode shapes. However, natural frequencies can exhibit a low sensitivity to certain types of damage, especially when compared to their sensitivity to temperature. Strain mode shapes though have been proved to be more sensitive to local damage and less to temperature than natural frequencies. In this work, an FRP footbridge is subjected to periodic annual VBM. The dynamic strains of the bridge are monitored with embedded Fiber-optic Bragg Gratings (FBG), a type of FOS that allows determining natural frequencies and strain mode shapes accurately from low-amplitude strain data. Vibration modes are identified from the dynamic strains and the influence of temperature and heavy loading is investigated. The identified modes are used for calibrating a finite element model. Possible damage scenarios on FRP structures are simulated and their influence on modal characteristics is investigated and compared to this of temperature.
This paper presents an algorithm for a time series analysis—the high-order multivariate autoregressive model M-AR(P). The theory of the model and the application in modal parameter identification of structures excited by natural random forces is described. A water transmission tower is taken as a real example to illustrate the application procedure of this algorithm. Good results in natural frequencies, damping ratios, mode shapes, as well as power spectra and coherence functions identified by this model prove the advantages and usefulness of the algorithm.
The lack of a physically intuitive OMAX approach can be attributed to the difficulty of decomposing the measured joint response in a forced and an ambient part in an accurate way. In this paper, a matrix projection (subspace) approach is developed that achieves this decomposition. It allows to use any experimental and operational modal analysis technique on the forced and ambient parts of the data, respectively, and to combine them for joint modal parameter estimation; here, this is further elaborated for subspace identification. An extensive simulation example illustrates the accuracy and practicability of this approach.
Modal parameter estimation requires a lot of user interaction, especially when parametric system identification methods are used and the modes are selected in a stabilization diagram. In this paper, a fully automated, generally applicable three-stage clustering approach is developed for interpreting such a diagram. It does not require any user-specified parameter or threshold value, and it can be used in an experimental, operational, and combined vibration testing context and with any parametric system identification algorithm. The three stages of the algorithm correspond to the three stages in a manual analysis: setting stabilization thresholds for clearing out the diagram, detecting columns of stable modes, and selecting a representative mode from each column. An extensive validation study illustrates the accuracy and robustness of this automation strategy.
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No abstract is provided for this article.
No abstract is provided for this article.
