Delamination Detection in Concrete Plates using Output-only Vibration Measurements

In this paper, system identification of a cable-stayed bridge in Korea, the Hwamyung Bridge, is performed using vibration responses measured by a wireless sensor system. First, an acceleration based-wireless sensor system is employed for the structural health monitoring of the bridge, and wireless sensor nodes are deployed on a deck, a pylon and several selected cables. Second, modal parameters of the bridge are obtained both from measured vibration responses and finite element (FE) analysis. Frequency domain decomposition and stochastic subspace identification methods are used to obtain the modal parameters from the measured vibration responses. The FE model of the bridge is established using commercial FE software package. Third, structural properties of the bridge are updated using a modal sensitivity-based method. The updating work improves the accuracy of the FE model so that structural behaviors of the bridge can be represented better using the updated FE model. Finally, cable forces of the selected cables are also identified and compared with both design and lift-off test values.

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

Modified frequency and spatial domain decomposition method based on maximum likelihood estimation

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

Vibration‐based structural health monitoring (SHM) is crucial in assessing the integrity of large civil infrastructures. Developing an accurate finite element (FE) model that effectively reflects the dynamics of a full‐scale structure is essential for the success of vibration‐based SHM. This study introduces a comprehensive framework for FE model updating and validation of a truss railway bridge. The framework integrates operational modal analysis, a sensitivity‐based FE model updating approach, and the dynamics of train‐track‐bridge interaction (TTBI). The research focused on a 36‐meter‐long steel truss railway bridge, which underwent testing under both static and dynamic loads caused by train transit. For modal identification, the study utilized the covariance‐driven stochastic subspace identification (Cov‐SSI) method in conjunction with hierarchical density‐based spatial clustering of applications with noise (HDBSCAN). Results indicated that the sensitivity‐based FE model updating significantly enhanced model accuracy, reducing the average frequency errors from 11% to much lower 3%. In addition, the FE model was validated through static load tests using an EMD GT26 locomotive. In the dynamic validation phase, the vertical TTBI was modelled. Comparatively, the acceleration time histories from the TTBI model, exhibiting a normalized mean absolute error (nMAE) of 36%, aligned more closely with actual measurements than those from the basic moving load model, which had a higher nMAE of 45%. This framework provides an accurate numerical platform for load rating and SHM activities.

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.

The paper summarizes the dynamic-based assessment of a reinforced concrete arch bridge, dating back to the 50`s. The outlined approach is based on ambient vibration testing, output-only modal identification and updating of the uncertain structural parameters of a finite element model. The Peak Picking and the Enhanced Frequency Domain Decomposition techniques were used to extract the modal parameters from ambient vibration data and a very good agreement in both identified frequencies and mode shapes has been found between the two techniques. In the theoretical study, vibration modes were determined using a 3D Finite Element model of the bridge and the information obtained from the field tests combined with a classic system identification technique provided a linear elastic updated model, accurately fitting the modal parameters of the bridge in its present condition. Hence, the use of output-only modal identification techniques and updating procedures provided a model that could be used to evaluate the overall safety of the tested bridge under the service loads.

While linear finite element (FE) model updating has been successfully applied for predicting structural damage as loss of effective stiffness, nonlinear FE model updating can provide improved and more accurate damage identification (i.e., a more comprehensive measure of damage) and can additionally be used as a tool for damage prognosis. The current study is focused on characterizing the nonlinear material behavior in a FE model of a three-story infilled frame using the identified instantaneous natural frequencies and mode shapes. The 2/3-scale, 3-story, 2-bay reinforced concrete frame with masonry infills was subjected to large amplitude earthquake base excitations on a shake table. The deterministic stochastic subspace identification method is used for estimating the instantaneous (during short-time windows) modal parameters of the structure based on the nonlinear response of structure during a seismic base excitation. Parameters of a priori selected hysteretic models (Bouc-Wen) at different finite elements of the structural model are calibrated to minimize the misfit between the identified modal parameters and those from the FE model. The accuracy of the calibrated FE model is assessed through the comparison of the predicted response and natural frequencies obtained from the model with those of the specimen.

Dynamic study is important in order to design, repair and rehabilitation of structures. It has played an important role in the behavior characterization of structures; such as: bridges, dams, high rise buildings etc. There had been substantial development in this area over the last few decades, especially in the field of dynamic identification techniques of structural systems. Frequency Domain Decomposition (FDD) and Time Domain Decomposition are most commonly used methods to identify modal parameters; such as: natural frequency, modal damping and mode shape. The focus of the present research is to study the dynamic characteristics of typical timber masonry walls commonly used in Portugal. For that purpose, a multi-storey structural prototype of such wall has been tested on a seismic shake table at the National Laboratory for Civil Engineering, Portugal (LNEC). Signal processing has been performed of the output response, which is collected from the shaking table experiment of the prototype using accelerometers. In the present work signal processing of the output response, based on the input response has been done in two ways: FDD and Stochastic Subspace Identification (SSI). In order to estimate the values of the modal parameters, algorithms for FDD are formulated and parametric functions for the SSI are computed. Finally, estimated values from both the methods are compared to measure the accuracy of both the techniques.

Identification, Model Updating, and Validation of a Steel Twin Deck Curved Cable‐Stayed Footbridge

The ancient masonry city hall in L'Aquila, Italy (Margherita Palace) that was severely damaged during the L'Aquila earthquake in April 2009 has been subjected to ambient vibration tests to determine its dynamic characteristics such as the natural frequencies, mode shapes, and damping ratios. The processing of ambient vibration responses is performed with three output-only identification approaches: (1) the frequency-domain decomposition, (2) random decrement (RD) technique combined with the eigensystem realization algorithm (ERA), and (3) the natural excitation technique (NExT) combined with ERA. Measurements were made in the palace and in the civic tower using 15 sensors in three different configurations, and the ambient vibration tests involved the simultaneous measurements of longitudinal and lateral vibrations. Lateral vibration modes for the civic tower were identified clearly; a total of five modal shapes and frequencies were identified in the frequency range 0 to 7 Hz. The modal identification in the palace was more difficult because of the severe damage; however, the two bending modes in the perpendicular directions were identified. The comparison of the three experimental techniques shows good agreement, confirming the reliability of the three identification methods. The results of the modal identification have been used to update the finite-element model of the palace. DOI: 10.1061/(ASCE)ST.1943-541X.0000494. © 2012 American Society of Civil Engineers. CE Database subject headings: Historic buildings; Masonry; Finite element method; Vibration; Structural analysis; Earthquakes; Italy. Author keywords: Output-only modal identification; Frequency domain decomposition (FDD); Eigensystem realization algorithm (ERA); Random decrement (RD); Masonry structure; Finite-element model; Ambient vibration; Identification; EMA; Operational modal analysis.

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.

In this paper, a finite element model updating (FEMU) method is proposed based on the response surface model (RSM) and genetic algorithm (GA) to establish a high-precision finite element (FE) model of space station scientific experiment racks. First, the fine solid and mixed FE models are established, respectively, and a comparison of the modal test results is conducted. Then, an orthogonal experimental design is used to analyze the significance of the parameters, and the variables to be modified are determined. The design parameters are sampled via the Latin hyperbolic method and are substituted into the FE model to obtain the modal parameters of the scientific experiment rack. The mapping relationship between the design and modal parameters is fitted by constructing the Kriging function, and the RSM is established. The design parameters of the scientific experiment rack are optimized via GA, and the initial FE model is updated, which has the advantage of improving the computing efficiency. Finally, the updated FE model of the experiment rack is verified by frequency sweep and random vibration tests. The experimental results show that the proposed approach has high precision and computing efficiency, and compared with the test results, the modal frequency errors of the updated model are within 5%, and the vibration response errors under random excitation of the updated model are within 7%.

Frequency Domain Decomposition (FDD) is an operational modal analysis method by which the dynamic parameters of structures are obtained using response signals recorded on several locations of structures. In recent years, many researchers have employed the method to estimate the modal parameters of several Multi-Degree-of-Freedom (MDOF) mechanical and civil systems. The accuracy of the results depends on recording and signal processing parameters such as sampling frequency, windowing, filtering etc. As a result, investigation on the influence of these parameters on the accuracy of the estimates is a good practice. In this research, the uncertainty of modal frequencies obtained from FDD method is analyzed. In order to achieve this goal, the exact values of modal parameters must be available to be compared with the results from FDD method. For this purpose, synthetic signals with random characteristics the same as the ambient vibrations’ are produced and structures with known dynamic parameters are simulated and loaded. The effect of several parameters are investigated in the accuracy of results and proper values and settings are proposed to minimize the errors.

This paper reports the step-by-step procedures of a full-scale ambient vibration test and the corresponding modal identification and Bayesian structural model updating of a coupled building. The building is characterized as a combination of a main part and a complementary part connected together by corridors in between. Compared with the main part, the volume of the complementary part is much smaller. Therefore, the influence on the dynamic properties of the complementary part from its counterpart is expected. To capture the dynamic properties of the coupled building, a 21-setup ambient vibration test was designed to cover all the degrees of freedom (DOFs) of interest. The modal parameters of each setup were identified following the frequency domain decomposition (FDD) method and the partial mode shapes from different setups were assembled following a least-squares method. To determine the stiffness of the linkage between the two parts, the coupled building was simulated with two linked shear buildings and updated utilizing the Markov chain Monte Carlo (MCMC)-based Bayesian model updating method. The identified modal parameters revealed interesting features about the coupled effects between the main part and complementary part and were discussed in detail. The good match between the model-predicted and identified modal parameters verified the validity of proposed shear building model. This study provides valuable experience in the area of structural model updating and structural health monitoring.