Non-contact based structural damage assessment using stochastic subspace identification and finite element model updating.

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.

In the present paper, Structural health monitoring has become an evolving area of research in last few decades with increasing need of online monitoring the health of large structures. The damage detection by visual inspection of the structure can prove impractical, expensive and ineffective in case of large structures like offshore platforms, multistoried buildings and bridges. Structural health monitoring is defined as the process of detecting damage in a structural system. Damage in the system causes a change in dynamic properties of a system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. Structural damage detection and damage localization of jacket platforms, based on wavelet packet transforms is presented in this paper. Dynamic signals measured from the structure by the finite element software package ANSYS are first decomposed into wavelet packet components. Component energies are then calculated and used for damage assessment. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and location.

The functionality of piezoelectric devices is of significant importance in the electromechanical impedance (EMI)‐based structural health monitoring (SHM) and damage detection. Despite the previous work, the EMI response characteristics of a degraded piezoelectric‐based smart interface have not been sufficiently investigated due to the difficulty in making realistic functional defects via the experiment. To overcome this issue, we present a predictive simulation strategy to comprehensively investigate the EMI response characteristics of a smart interface subjected typical functional degradations. For that, a bolted steel girder connection is selected as a host structure to experimentally conduct EMI response measurement via the smart interface. Then, a finite element (FE) model corresponding to the experimental model is established and updated to reproduce the measured EMI response. By using the updated FE model, four common degradation types, including shear lag effect, transducer debonding, transducer breakage, and interface detaching are simulated and their effects on the EMI response are comprehensively analyzed. It is found that the interface detaching defect has significant impacts on the primary resonances of the EMI response and generates additional peaks with complex modal shapes. Also, the functional defects can result in distinctive EMI response characteristics, which are promising for assessing the functional condition of the smart interface.

Recently, many structural damage detection (SDD) methods have been proposed to monitor the safety of structures. As an important modal parameter, mode shape has been widely used in SDD, and the difference of vectors was adopted based on sensitivity analysis and mode shapes in the existing studies. However, amplitudes of mode shapes in different measured points are relative values. Therefore, the difference of mode shapes will be influenced by their amplitudes, and the SDD results may be inaccurate. Focus on this deficiency, a multi-strategy SDD method is proposed based on the included angle of vectors and sparse regularization in this study. Firstly, inspired by modal assurance criterion (MAC), a relationship between mode shapes and changes in damage coefficients is established based on the included angle of vectors. Then, frequencies are introduced for multi-strategy SDD by a weighted coefficient. Meanwhile, sparse regularization is applied to improve the ill-posedness of the SDD problem. As a result, a novel convex optimization problem is proposed for effective SDD. To evaluate the effectiveness of the proposed method, numerical simulations in a planar truss and experimental studies in a six-story aluminum alloy frame in laboratory are conducted. The identified results indicate that the proposed method can effectively reduce the influence of noises, and it has good ability in locating structural damages and quantifying damage degrees.

Numerical and experimental analysis of uncertainty on modal parameters estimated with the stochastic subspace method

This study introduces a Bayesian model updating approach for analyzing the long-term performance of steel–concrete composite beams (SCBs). Two nominally identical SCBs (SCB-1 and SCB-2) were designed, fabricated, and subjected to modal testing. Despite their identical design parameters, notable differences were observed in their frequencies and mode shapes during testing. Initial finite element model (FEM) analyses, developed under testing conditions, revealed notable discrepancies between theoretically computed values and the results of modal testing. To resolve these differences, the FEM was updated using the Bayesian approach, integrating the dynamic test data to enhance model accuracy. The updated FEM was subsequently employed to assess the long-term performance of the SCBs, with a particular focus on the time-dependent effects of concrete shrinkage and creep in deflection calculations. The findings reveal substantial differences in the long-term deflection predictions between the updated and initial FEM. Specifically, the long-term deflection of SCB-1 increased by 13.1%, whereas that of SCB-2 decreased by 9.8%, leading to an overall difference of 25.3% between the two beams. These findings underscore the considerable impact of fabrication errors and material inhomogeneities on structural performance, highlighting the limitations of initial FEM based solely on test parameters in accurately capturing actual behavior. Consequently, the study emphasizes the critical role of model updating in accurately predicting the long-term performance of SCBs.

Most finite element model updating (FEMU) studies on bridges are acceleration-based due to their lower cost and ease of use compared to strain- or displacement-based methods, which entail costly experiments and traffic disruptions. This leads to a scarcity of comprehensive studies incorporating strain measurements. This study employed the strain- and acceleration-based FEMU analyses performed on a more than 50-year-old multi-span concrete highway viaduct. Mid-span strains under heavy vehicles were considered for the strain-based FEMU, and frequencies and mode shapes for the acceleration-based FEMU. The analyses were performed separately for up to three variables, representing Young's modulus adjustment factors for different groups of structural elements. FEMU studies considered residual minimisation and the error-domain model falsification (EDMF) methodology. The residual minimisation utilised four different single-objective optimisations focusing on strains, frequencies, and mode shapes. Strain- and frequency-based FEMU analyses resulted in an approximately 20% increase in the overall superstructure's design stiffness. This study shows the benefits of the intuitive EDMF over residual minimisation for FEMU, where information gained from the strain data, in addition to the acceleration data, manifests more sensible updated variables. EDMF finally resulted in a 25-50% overestimated design stiffness of internal main girders.

Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

Correction of stiffness and mass matrices utilizing simulated measured modal data

Chapter 1 - Introduction

The paper focuses on damage detection of civil engineering structures and especially on concrete bridges. A method for structural health monitoring based on vibrational measurements is presented and discussed. Experimentally identified modal parameters (eigenfrequencies, mode shapes and modal masses) of bridge structures are used to calculate the inverse stiffness matrix, the so-called flexibility matrix. By monitoring of the stiffness matrix, damage can easily be detected, quantified and localized by tracking changes of its individual elements. However, based on dynamic field measurements, the acquisition of the flexibility matrix instead of the stiffness matrix is often the only choice and hence more relevant for practice. But the flexibility-based quantification and localisation of damage are often possible but more difficult, as it depends on the type of support and the location of the damage. These issues are discussed and synthetized, that is an originality of this paper and is believed useful for engineers in the damage detection of different bridge structures. First the theoretical background is briefly repeated prior to the illustration of the differences between stiffness and flexibility matrix on analytical and numerical examples. Then the flexibility-based detection is demonstrated on two true bridges with real-time measurement data and the results are promising.

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.

Efficient wind energy harvesting becomes more and more important as a consequence of the increasing interest in renewable energy in the European Union [1]. This leads to growing sizes of wind turbines (WTs), and with it, larger WT blades (WTBs). The structural designs of these WTBs are created to optimize the potential energy output, where low mass is a key requirement. However, high flexibilities and lower buckling capacities are further results of these developments [2], thus certain damage scenarios become significant. Intelligently designed structural health monitoring (SHM) systems can help to reduce the associated operations and maintenance costs. Even though, several techniques are already developed for structural damage detection (SDD) in WTBs, the majority of these methods is not suitable for inservice measurements. This paper presents a SDD and structural damage localization (SDL) method based on the partial autocorrelation function (PACF) of vibration responses. The approach is applied to a numerical model of a large WTB, where the acceleration responses are obtained from transient dynamic simulations with a simplified aerodynamic loading approach. The novel damage sensitive feature (DSF) is developed as the Mahalanobis distance between a baseline and current vector of PACF coefficients. First, numerical modal analysis of the finite element (FE) WTB model is performed in order to estimate the effect of a disbonding damage scenario on the vibration characteristics. Second, the behaviour of the PACF for time series of the healthy system is discussed. Third, the SDD results on the basis of statistical hypothesis testing are assessed for two selected sensor locations and increasing damage extents. Finally, the performance of the proposed DSF with respect to SDL is illustrated for multiple locations on the WTB’s surface. This study demonstrated the efficiency of a DSF based on the PACF for SDD and SDL, which is promising for future developments of vibration-based SHM techniques in WTBs.

Structural Health Monitoring (SHM) programs play an essential role in the field of civil engineering, especially for assessing safety conditions involving large structures such as viaducts, bridges, tall buildings, towers, and old historical buildings. Typically, an SHM process needs to be based on a trustful strategy for detecting structural novelties or abnormal behaviors. Usually, such an approach is complemented with human inspection and structural instrumentation routines, where the latter requires proper hardware equipment and software tools. However, most strategies already published in this topic mainly focus on modal identification procedures and tracking their outputs i.e., structural modal parameters. Such approaches usually lead to high computational costs and can still be insensitive to minor changes in structural behavior, thus missing crucial damage scenarios in their initial manifestations. To circumvent these drawbacks, recent studies showed that the use of symbolic representations derived directly from raw time-domain data (e.g., acceleration measurements) obtained from vibration tests could provide more damage-sensitive responses with lower computational effort. The proposed methodology employs a clustering technique over such symbolic representations within a moving time-window framework and uses a single-valued index to indicate if a novelty (i.e., structural damage and/or interventions, such as maintenance/repair) is present in the acquired data. Two practical studies—a highway and a railway bridge located in France—show that the proposed tool provide an unsupervised and adaptive scheme for automated real-time SHM applications based on vibration tests.