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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.
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The modal analysis of mechanical or civil engineering structures consists of three steps: data collection, system identification and modal parameter estimation. The system identification step plays a crucial role in the quality of the modal parameters, that are derived from the identified system model, as well as in the number of modal parameters that can be determined. This explains the increasing interest in sophisticated system identification methods for both experimental and operational modal analysis. In purely operational or output-only modal analysis, absolute scaling of the obtained mode shapes is not possible and the frequency content of the ambient forces could be narrow banded so that only a limited number of modes are obtained. This drives the demand for system identification methods that take both artificial and ambient excitation into account so that the amplitude of the artificial excitation can be small compared to that of the ambient excitation. An accurate, robust and efficient system identification method that meets this requirements is combined deterministic–stochastic subspace identification. It can be used both for experimental modal analysis and for operational modal analysis with deterministic inputs. In this paper, the method is generalized to a reference-based version which is faster and, if the chosen reference outputs have the highest SNR values, more accurate than the classical algorithm. The algorithm is validated with experimental data from the Z24 bridge that overpassing the A1 highway between Bern and Zurich in Switzerland, that have been proposed as a benchmark for the assessment of system identification methods for the modal analysis of large structures. With the presented algorithm, the most complete set of modes reported so far is obtained.
This paper presents a case of object motion tracking where a real-time kinematic Global Positioning System (RTK-GPS) is used for correction of data obtained from an inertial navigation system (INS). The displacements obtained from the RTK-GPS and the accelerations obtained from the INS are combined using an unscented Kalman filter (UKF). The combined data are used to assess the motion of a 6 MW turbine blade during a hoisting operation and are compared to the blade motion obtained by use of three Leica total stations, that have been used for verification. Special focus in this paper goes to merging of heterogeneous data (displacements, rotations, and accelerations) obtained from different measurement systems. Analysis of the motion data shows that the RTK-GPS/INS measurement system allows for very accurate recording of displacement and rotation measurements.
During the last decade, many vibration-based structural health monitoring systems have been successfully implemented in different structures such as bridges, towers, stadia and wind turbines, with the aim of studying the structure dynamics and its evolution over time, eventually detecting the occurrence of novel structural behaviour that may indicate the presence of damage. Such vibration-based monitoring systems generally rely on the identification of modal properties, which are then used as monitoring features. Therefore, from operational modal analysis to the tracking of those features and finally to data normalization, many processing steps occur that depend on the accuracy of the identified modal properties. Thus, the estimation of the uncertainties associated with the identified modal properties increases the robustness of this process. In this context, data obtained from the continuous dynamic monitoring of a concrete arch dam has been used to evaluate the gains of quantifying the uncertainties of modal properties, evaluating in particular the effect of taking these uncertainties into consideration when performing automated operational modal analysis, modal tracking and data normalization. Nevertheless, it is observed that the most significant gains of considering estimates uncertainties occur when these quantities are used for removing outliers during modal tracking.
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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.
In this work, a procedure for damage localization and quantification in beam structures is presented. The proposed technique exploits the variations of the curvature between healthy and damaged structures, computed from the flexibility matrix. The flexibility matrix is approximated using the lowest structural frequencies and mode shapes, which can be easily derived on-site from vibration-based monitoring of the structure. Since in real-case scenarios structures can be instrumented only with a limited number of sensors, a procedure based on the expansion of the measured modal components is proposed. The presented technique is verified by testing two beam structures affected by different damages in terms of position and extension.
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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.
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