Structural identification of a cantilever beam with multiple cracks: Modeling and validation

Structural safety assessment of bowstring type RC arch bridges using ambient vibration testing and finite element model calibration

Estimating electromechanical oscillation modes from synchrophasor measurements in China Southern Power Grid

The interest in damage identification methods has increased significantly in recent years due to the rising demand for structural health monitoring of structures. This study presents an enhanced version and validation of a recently introduced method for damage detection, localization and quantifying damage using vibration data. The method is validated through a building application, a scaled steel frame model built in the laboratory. The validation is carried out using eight different damage scenarios in numerical and experimental studies. These studies are based on finite element analysis and ambient vibration tests. A newly introduced filtering approach that utilizes MAC rejection levels in Modal Participation Ratio derivation is provided to replace the user-controlled bandpass filter to obtain more reliable vibration data in experimental investigations. The results showed that the proposed procedure is more capable of correctly detecting, localizing and quantifying damage to a building, considering the real-life conditions.

Visualization and Dimension Reduction of High Dimension Data for Structural Damage Detection

Damping estimation using free decays and ambient vibration tests

This paper investigates the use of conventional operational modal analysis (OMA) techniques for modal parameters identification of a rotating system supported by roller bearings. Although largely applied in civil engineering, in-depth studies on different types of systems are still limited in the literature. The novel of the paper is to address such issue by applying conventional OMA methods, such as enhanced frequency domain decomposition (EFDD) and stochastic subspace identification (SSI-data), to identify the modal parameters of a rotating machine, investigating the challenging particularities of these systems due to their inherent operating conditions, especially regarding the presence of harmonic forces, eventually closed-spaced modes, and non-proportional damping due to the bearings. The results presented in the experimental tests showed that, with the use of specific tools, in comparison with traditional experimental modal analysis (EMA), the used OMA methods have managed to successfully identify the modal parameters of a roller bearing supported rotor.

This paper presents the operational modal analysis of a 90-m-high RC minaret of an iconic mosque considered as a landmark of the city. The minaret was monitored for three days with 11 tri-axial MEMS accelerometers. The purpose of the study was to observe the behavior, develop a representative finite element (FE) model, and establish baseline data for health monitoring studies. The modal properties were extracted using three operational modal analysis techniques (OMA): Enhanced Frequency Domain Decomposition (EFDD), Stochastic Subspace Identification (SSI), and Natural Excitation Technique with Eigensystem Realization Algorithm (NExT-ERA). The first 10 identified modes were below 7 Hz. Eight modes out of the ten were bending-dominant, while the remaining two were torsion-dominant. A FE model was also developed in ETABS to ascertain and compare the response of the structure with the identified results. From the FE model, the modes corresponding to the first ten identified modes were considered for comparison with the identified frequencies from ambient monitoring. The maximum 7.71% error was observed between the experimental and numerical frequencies. The error was minimized by using the manual updating the material properties and adding the weight of nonstructural elements. The variation of identified modal frequencies with ambient temperature was observed to be linearly dependent to a reasonable degree. A general trend of decreasing identified frequencies was observed with the rise in temperature.

Eigensystem Realization Algorithm (ERA), Stochastic Subspace Identification (SSI), Continuous Wavelet Transform (CWT), and Enhanced Frequency Domain Decomposition (EFDD) are four widely used damping identification methods. Their performance remains unclear in previous reviews and comparative discussions. This uncertainty can be attributed to three critical factors: the varying behavior of different methods under different excitations, the lack of a clear benchmark for evaluating accuracy, and the unquantified influence of parameter tuning on results. This study revisits and evaluates these methods under controlled conditions and addresses the key challenges that hinder reliable damping identification. A major challenge identified is the sensitivity of the results to parameter settings, which significantly impacts the stability and accuracy of the identification. Based on the evaluation, recommended methods and corresponding parameter guidelines are provided for three common excitation scenarios: impulse, white noise, and earthquakes. This study offers practical guidance for the selection and application of damping identification methods in structural dynamics.

This paper addresses delamination detection of concrete slabs by analyzing vibration data from dynamic tests. Four concrete decks with different delamination sizes were constructed and experimental tests were conducted on these concrete slabs. Traditional peak-picking (PP), frequency domain decomposition (FDD) and stochastic subspace identification (SSI) methods were applied in the modal identification from real measurements of dynamic velocity responses. The modal parameters identified by these identification methods matched very well. The changes in modal frequencies, damping ratios, and mode shapes that were extracted from dynamic measurements were correlated to delamination size and can indicate presence and severity of delamination. Finite element (FE) models of reinforced concrete plates with different delamination sizes and locations were established. The modal parameters computed from FE models were compared to those obtained from real measurements and FE models were validated.KeywordsMode ShapeDynamic TestConcrete SlabModal IdentificationConcrete DeckThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Modal analysis is a fundamental and essential research direction in the field of structural engineering. The ultimate goal is to determine the modal parameters of the structures. However, the existing modal analysis algorithms often require a large number of parameter adjustments and manual intervention during operation, which cannot be fully automated. In order to realize the automatic identification of modal parameters, the automatic operational modal identification method (AOMI) is proposed based on the interpolated power spectral density estimation (IPSE). To achieve more precise spectrum analysis in the low-frequency band, IPSE is employed to perform Fourier transform on the original frequency domain segment with optimized frequency resolution. This enhances the sharpness of the obtained spectrum in the low-frequency range, making peak frequencies more discernible. Subsequently, the scale-space peak picking algorithm is used to automatically obtain the peak of the power spectral density (PSD), thus enabling the automatic identification of the natural frequency. Finally, the frequency domain decomposition method (FDD) is used to identify modal parameters based on the natural frequencies. The effectiveness of AOMI is verified through the modal identification of the old steel truss bridge and the three layer framework. Under the environmental excitation, the frequencies identified by the IPSE method is close to that of FDD, Bayesian fast fourier transform (FFT) and covariance driven stochastic subspace identification (SSI-COV). Furthermore, the PSD obtained through IPSE has sharper peak than that of FDD and the Welch’s method. The damping ratio identification accuracy and modal assurance criterion (MAC) are satisfactory in AOMI, which can improve the automatic performance.

With the rapid development of underground engineering in China, the heavy structural maintenance work followed is expected to be a great challenge in the future. The development also provides a promising application prospect for the newly developed vibration‐based health assessment and monitoring methods. However, the fact that tunnels are embedded in soil makes collecting and identifying the vibration characteristics more difficult, especially for the online monitoring. In this paper, a new identification method that combines the natural excitation technique (NExT) and stochastic subspace identification (SSI) method is developed. The new method is compared with the traditional SSI method, and mode frequency analysis is made based on a series of field tests carried out at the subway and power tunnel. It is found that both stability and efficiency of the mode frequency identification have been greatly improved, and it more suitable for online monitoring. Meanwhile, a mathematical model is used to analyze the original mode characteristics and the influence of soil coupling. The results are also compared with the field tests results by using the NExT‐SSI method, and some recommendations are also made for how to choose the vibration modals for vibration‐based monitoring in the tunnel.

Structural health monitoring (SHM) system is an essential part of large structures to allow for early detection of possible damages or defects and avoid human or economic losses. In this paper, stochastic subspace identification (SSI) is employed as a means to obtain the main characteristics of the structure i.e., mode shapes, natural frequencies, etc. Features obtained from SSI are used to assess the health status of the structure. The proposed method is implemented on a scaled three-story laboratory structure where structural damping is altered using a novel magnetic approach. Finite-element analysis is employed to validate the experimental results.

This paper presents an enhanced version and the validation of a recently proposed methodology for earthquake-induced damage detection and localization in masonry towers by using long-term vibration monitoring data. The proposed enhanced method is based on continuous operational modal analysis through stochastic subspace identification, dynamic multiple linear regressive analysis to remove the effects of changing environmental conditions and finite element (FE) model updating. Any anomalous frequency deviation from normal conditions resulting from an earthquake triggers the damage localization process. This task is performed by solving an inverse FE model calibration problem, where equivalent elastic properties of macrostructural elements are identified by minimizing an objective function considering experimentally identified and numerically predicted damage-induced decays in natural frequencies and changes in eigenvector components. To minimize the computational effort of this calibration procedure, a quadratic surrogate model is constructed using a tuned numerical FE model of the structure. The methodology is validated through application to the “Sciri Tower”, an historic civic masonry tower located in Perugia, Italy, that has been continuously monitored by the authors for more than 1 year. The validation is carried out by using simulated damage scenarios and a set of real far-field earthquake data and is based on a FE model of the tower including surrounding buildings, calibrated on the basis of the measured data from ambient vibration tests. The results demonstrate that the proposed procedure is capable of correctly detecting and localizing earthquake-induced damages.

This paper presents the analytical modeling, modal testing, and finite-element model updating for a two-span masonry arch bridge. An Ottoman masonry arch bridge built in the 19th century and located at Camlihemsin, Rize, Turkey is selected as an example. Analytical modal analysis is performed on the developed 3D finite-element model of the bridge to obtain dynamic characteristics. The ambient vibration tests are conducted under natural excitation such as human walking. The operational modal analysis is carried out using peak picking method in the frequency domain and stochastic subspace identification method in the time domain, and dynamic characteristics (natural frequencies, mode shapes, and damping ratios) are determined experimentally. Finite-element model of the bridge is updated to minimize the differences between analytically and experimentally estimated dynamic characteristics by changing boundary conditions. At the end of the study, maximum differences in the natural frequencies are reduced on average from 18 to 7% and a good agreement is found between analytical and experimental dynamic characteristics after finite-element model updating.

The identification of modal parameters of a machine tool structure, in service, is important to ensure stability and productivity during machining operations. The characterization can be carried out through an Operational Modal Analysis (OMA). However, in the presence of strong harmonic excitation, the application of OMA is not straightforward. To overcome this difficulty, the Transmissibility Function-Based (TFB) method is proposed. The major advantage of this approach is its independence from the excitation nature and its ability to separate structural poles from spurious ones. The main novelty of this paper lies in the investigation of the TFB approach to identify the modal properties of a machine tool, during machining operations. Identified modal model through an Experimental Modal Analysis (EMA) of the considered machine tool, at rest, presents our reference modal base to validate results obtained through the TFB approach. For a comparison purpose, the modified Enhanced Frequency Domain Decomposition (EFDD) method is also investigated. Both methods enable the identification of the modal properties under operational conditions, with a clear advantage to the TFB approach due to its ability to eliminate all of the preponderant harmonic components from the measured data without the need of any additional selection criteria. The TFB method is thus a reliable technique for the identification of modal parameters of a machine tool in operational conditions.