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As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames. [ABSTRACT FROM AUTHOR]

As a critical component of substations, the substation frames are characterized by significant height and span, which presents substantial challenges and risks in conducting dynamic response tests using traditional sensors. To simplify these difficulties, this paper introduces an experimental method utilizing MEMS sensor-based vibration acquisition. In this approach, smartphones equipped with MEMS sensors are deployed on the target structure to collect vibration data under environmental excitation. This method was applied in a dynamic field test of a novel composite substation frame. During the test, the proposed MEMS-based vibration acquisition method was conducted in parallel with traditional ultra-low-frequency vibration acquisition methods to validate the accuracy of the MEMS data. The results demonstrated that the MEMS sensors not only simplified the testing process but also provided reliable data, offering greater advantages in testing convenience compared with traditional contact methods. The modal parameters of the substation frame, including modal frequencies, damping ratios, and mode shapes, were subsequently identified using the covariance-driven stochastic subspace identification method. The experimental methodology and findings presented in this paper offer valuable insights for structural dynamic response testing and the wind-resistant design of substation frames.

This paper presents results of an experimental study that focuses on monitoring changes in dynamic characteristics of a damaged and retrofitted two-story reinforced concrete (RC) building model with poor detailing. In the study, ambient vibration tests are performed via four test setup protocols in order to monitor changes in dynamic characteristics before and after such setups. The four main test setups are: (i) undamaged (as-built) case (Setup # 1), (ii) damaged cases with different degrees of damages (Setup # 2), (iii) repaired case with different types of repair methods (Setup # 3), and (iv) strengthened case with externally bonded CFRP composite laminates (Setup # 4). In addition, a total of eight ambient vibration tests are conducted in order to assess the effects of degree of damage and associated retrofit systems on building's dynamic characteristics such as natural frequencies, mode shapes, and damping ratios. The Stochastic Subspace Identification (SSI) Method is used to identify the dynamic characteristics. Experimentally identified dynamic characteristics obtained from all setups are compared with each other in order to detect the effect of damage and repair applications on the structure's dynamic characteristics. Moreover, Modal Assurance Criterion (MAC) and Coordinate Modal Assurance Criterion (COMAC) are determined to examine the changes of stiffness of each RC building model. [ABSTRACT FROM AUTHOR]

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This paper aims at assessing the influence of environmental parameters on the modal characteristics of age–old masonry constructions. The results of a long–term ambient vibration monitoring of the San Frediano bell tower in Lucca (Italy) are reported. The tower, dating back to the 11th century, has been fitted along its height with four triaxial seismometric stations, which were left active for about one year. Data from the monitoring system have been processed via the Stochastic Subspace Identification Method in order to identify the tower’s modal characteristics and their variations over the year. The dependence of the tower’s frequencies on the ambient temperature was first studied and simulated via simple auto–regressive models. Then, some output–only models based on the principal component analysis (PCA) were applied, under the hypotheses of both linear and nonlinear (Kernel PCA) dependence of the natural frequencies on the unknown environmental parameters. The results indicate PCA to be an effective tool for detecting changes in the dynamic characteristics of masonry constructions. [ABSTRACT FROM AUTHOR]

The industrial structural systems always contain various kinds of nonlinear factors. Recently, a number of new approaches have been proposed to identify those nonlinear structures. One of the promising methods is the nonlinear subspace identification method (NSIM). The NSIM is derived from the principals of the stochastic subspace identification method (SSIM) and the internal feedback formulation. First, the nonlinearities in the system are regarded as internal feedback forces to its underlying linear dynamic system. The linear and nonlinear components of the identified system can be decoupled. Second, the SSIM is employed to identify the nonlinear coefficients and the frequency response functions of the underlying linear system. A typical SSIM always consists of two steps. The first step makes a projection of certain subspaces generated from the data to identify the extended observability matrix. The second one is to estimate the system matrices from the identified observability matrix. Since the calculated process of the NSIM is non-iterative and this method poses no additional problems on the part of parameterization, the NSIM becomes a promising approach to identify nonlinear structural systems. However, the result generated by the NSIM has its deficiency. One of the drawbacks is that the identified results calculated by the NSIM are not the optimal solutions which reduce the identified accuracy. In this study, a new time-domain subspace method, namely the nonlinear subspace-prediction error method (NSPEM), is proposed to improve the identified accuracy of nonlinear systems. In the improved version of the NSIM, the prediction error method (PEM) is used to reestimate those estimated coefficient matrices of the state-space model after the application of NSIM. With the help of the PEM, the identified results obtained by the NSPEM can truly become the optimal solution in the least square sense. Two numerical examples with local nonlinearities are provided to illustrate the effectiveness and accuracy of the proposed algorithm, showing advantages with respect to the NSIM in a noise environment. [ABSTRACT FROM AUTHOR]

The strong drive to harness wind energy has recently led to rapid growth of wind farm construction. Wind turbine towers with increased sizes and flexibility experience large vibrations. Structural health monitoring of wind turbines is proposed in the wind energy industry to ensure their proper performance and save maintenance costs. This study proposes a system identification method for vibration-based structural assessment of wind turbine towers. This method developed based on the stochastic subspace identification method can identify modal parameters of structures in operating conditions with harmonic components in excitations. It benefits wind turbine tower structural health assessment because classical operational modal analysis methods can fail as periodic rotation excitation from a turbine introduces harmonic disturbance to tower structure response data. The effectiveness, accuracy and robustness of the proposed method were numerically investigated and verified through a lumped-mass system model. The method was then applied to an in-service utility-scale wind turbine tower. The field testing campaign and modal parameter identification as well as structural assessment results were presented. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper investigates environmental effects on dynamic characteristics of isolated highway bridges constructed with the balanced cantilever method using ambient vibration test. The Gülburnu Highway Bridge located on the Giresun-Espiye state highway is selected as an application. Measurement time, frequency span and effective mode number are selected by considering similar studies found in the literature. The first measurement tests are conducted in June 2009 and traffic over the bridge is used as a source of ambient vibrations to obtain the dynamic characteristics such as natural frequencies, mode shapes and damping ratios. The second measurement tests are conducted in November 2011 using ambient vibrations and the dynamic characteristics are obtained, experimentally. The output-only modal identification of the bridge is effectively carried out using the enhanced frequency domain decomposition method in the frequency domain and stochastic subspace identification method in the time domain. At the end of the study, experimentally identified dynamic characteristics are compared with each other and environmental effects are investigated in detail. The analysis results reveal that the identified natural frequencies provide an effective indication for changes of dynamic characteristics of the bridge due to environmental effects. Significant variability in the identified natural frequencies is changing by up to maximum 14%. [ABSTRACT FROM PUBLISHER]

Abstract: This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used. [Copyright &y& Elsevier]

• The tower-line coupling effect is considered in the modal parameter identification. • An aeroelastic model of the tower-line system is fabricated. • An advanced 3D motion capture system is used to measure the response. • The parametric analysis is also carried out. The modal parameters of transmission tower-line systems constitute the basis of their wind and seismic design. However, such structures become complicated spatial systems due to the coupling effects between the towers and lines, making it difficult to evaluate the modal parameters of a transmission tower. To study the tower-line coupling effects on the frequencies and mode shapes of a tower, a stochastic subspace identification (SSI) method for identifying modal parameters is first introduced. Then, a finite element model of a transmission tower-line system is established, and the influences of three transmission line parameters, namely, the total span length, span ratio and height difference angle, on the modal parameters are analyzed. An aeroelastic model of the tower-line system is fabricated, and a percussion experiment is performed. The simulated and experimental results both show that the first-order frequency increases with an increase in the total span length but is only slightly affected by changes in the span ratio and the height difference angle. For the wind and seismic design of transmission towers, to obtain reliable natural vibration characteristics, a complete tower-line system model should be established instead of a single tower model. [ABSTRACT FROM AUTHOR]