Detection of Structural Damage Through Nonlinear Identification by Using Modal Testing

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

The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.

Nowadays, structural health monitoring has attracted much attention, due to the construction of important and complex structures and their safety. One branch of structural health monitoring is damage detection, estimating the location and extent of eventual damages in the structural systems. In this study, an efficient method is introduced to determine the location and extent of damage in the frame, beam and truss structures using time domain response and an optimization algorithm. First of all, the structural damage detection problem is formulated as a standard optimization problem. So that, the optimization objective function is defined by using the acceleration of damaged structures and analytical model acceleration. The acceleration is obtained using Newmark method. Damage is simulated by reducing the elasticity modulus of structural members. Then, damage problem converted to an optimization problem are solved by teaching–learning-based optimization (TLBO) algorithm therefore, the exact location and extent of damage in structure can be determined. In order to show the capabilities of the proposed approach for identifying structural damage, four illustrative test examples are considered with considering measurement noise effect. The results clearly show that the proposed method is as a powerful method to detect multiple damage in structures.

BackgroundDiagnosis and/or classification of a patient with axial Spondyloarthritis (SpA) generally relies on the presence of structural damage of the sacroiliac joints (SIJ) assessed by plain X-rays. However, the psychometric...

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.

Background/Objective: Early detection of damage in structures can prevent loss of lives and extend their service life. This has drawn considerable attention of researchers to applying Structural Health Monitoring (SHM) to detect structural damage. Wavelet Transform (WT) is a tool utilized to identify damage in structures by using structural responses. In this study, a WT damage detection method using Operational Deflection Shape (ODS) difference is presented. Method: A two-dimensional Continuous Wavelet Transform (CWT) is utilized to decompose the difference between the ODS of the intact and damaged structure to detect and locate damage. To demonstrate the ability of this technique, a numerical model of a steel plate is applied. Findings: The results show that the location of damage influences damage detectability when the decomposed signal is noisy. Improvements: This method eliminates the problem of border distortion experienced when conventional ODS is applied. The methodology shows the proposed techniques’ simplicity while the results show accuracy.Keywords: Cantilever Plate, Continuous Wavelet Transform, Damage Detection, Damage Severity Tree, Deflection Shape

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.

Vibration-based damage detection methods, leveraging structural response data to identify structural damage, play an important role in structural health monitoring, particularly for critical infrastructure like bridges and high-rise buildings. In recent years, deep learning, which can autonomously learn damage-sensitive features and classify damage from complex datasets, has been widely used in structural damage detection. This paper reviews deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and autoencoders, applied in vibration-based structural damage detection for civil structures over the past ten years, highlighting their advantages and disadvantages. The challenges and future trends for vibration-based damage detection by using deep learning are summarized.

Guided wave based detection of damage in honeycomb core sandwich structures

Structural nonlinear damage identification based on Bayesian optimization GNAR/GARCH model and its experimental study

Use of modal testing to identify damage on steel members

In structural health monitoring, effectively eliminating the influence of variable environmental conditions on modal frequencies remains a critical challenge for accurate damage identification. Nonstationary and nonlinear variations in modal frequencies, commonly induced by environmental changes, tend to overshadow the effects caused by structural damage. An improved Gaussian mixture model (GMM) is proposed in this paper to normalize nonlinear and nonstationary frequency data, enabling effective structural damage detection under variable environmental conditions. As the effectiveness of the GMM is highly influenced by the initial parameter values used in the expectation-maximization (EM) algorithm, a subdomain division strategy is first presented to determine the unique initial values of the GMM parameters. Through the application of the EM algorithm, the GMM is constructed simply and efficiently through the determined initial parameters. Next, on the basis of the constructed GMM, the modal frequency data are normalized to extract damage features that remain unaffected by environmental variations. Subsequently, Hotelling’s T2 statistic and its cumulative form are calculated for the damage features and designated as the damage indicators; meanwhile, the corresponding damage thresholds are also calculated according to the kernel density estimation technique. To validate the proposed method, two case studies are conducted: one with a numerical mass-spring system and the other with a real bridge structure. Results show that environmental influences no longer impact the normalized frequency data, and the cumulative statistic demonstrates outstanding accuracy in identifying structural damage.

A method is presented by which measured modes and frequencies from a modal test can be used to determine the location and magnitude of damage in a space struss structure. The damage is located by computing the Euclidean distances between the measured mode shapes and the best achievable eigenvectors. The best achievable eigenvectors are the projection of the measured mode shapes onto the subspace defined by the refined analytical model of the structure and the measured frequencies. Loss of both stiffness and mass properties can be located and quantified. To examine the performance of the method when experimentally measured modes are employed, various damage detection studies using a laboratory eight-bay truss structure were conducted. The method performs well even though the measurement errors inevitably make the damage location more difficult.

In this paper, an improvement of the elliptical triangulation method for damage detection using Lamb waves is presented. The damage is the main cause of structural failure and often occurs on structures. In order to avoid sudden failure, a special attention was given in the past decades to the damage detection in structures. In order to obtain efficient damage detection techniques, the structural health monitoring has been the main research topic of many scientists worldwide. The elliptical triangulation method, proposed in this paper, is a non-destructive method based on measurement of Lamb waves. This method, through the calculation of the time of flight of the signals and the actuator-sensor positioning, allows to identify position and dimension of the damage. The application of the method to the metallic structures and to the composite material structures is presented in this paper. The complexity connected with the uncertainty of the waves’ propagation speed due to the anisotropy of the composite materials has been explored through an iterative approach. The initialization of the wave propagation speed at first tentative iteration is the key issue for the convergence of the method. Seven different conditions were used to validate the method on both metallic and composite structures combining two damage shapes, two damage dimensions (effective damaged area), and three different positions. Upon evaluating the effectiveness, the method has been applied at two composite panels in order to detect by test the post-impact damages. Tests results have been compared with the numerical ones. The feasibility of the elliptical triangulation method to detect the damage (evaluating the damage position and area) has been proved using the ultrasonic C-Scan.

A damage detection process can be significantly enhanced if the nonlinear effects can be used when extracting damage-sensitive features from measured signals. The coherence function is typically used for nonlinearity identification by determining the extent of the output power linearly correlated with the input. However, the excitations are usually difficult to measure in actual tests. To overcome this limit, this article presents a signal-segments cross-coherence method for nonlinearity identification. By defining a signal-segments cross-coherence matrix and signal-segments cross-coherence index, the method can visually and quantitatively indicate the presence of nonlinearity. The innovation of the new method is that the coherence analysis process only depends on a single output signal, where input and baseline signals are not required. Then, a novel structural damage localization index is constructed by multi-point comparing of the signal-segments cross-coherence indices, based on the assumption that all the measured signals from different points on the structure have the same frequency bandwidth and components. To meet this requirement, a newly proposed signal decomposition method called analytic mode decomposition method is adopted. Numerical studies on a duffing oscillator and a 10 degree-of-freedom spring-damping-mass system were performed to demonstrate the nonlinear identification process and investigate the effectiveness and robustness of the signal-segments cross-coherence-based damage detection method. The results show that the signal-segments cross-coherence method can effectively indicate the appearance of nonlinearity by the signal-segments cross-coherence matrix and signal-segments cross-coherence index with strong noise robustness. And the proposed damage localization index can accurately detect the weak nonlinear damage even with severely noise-polluted signals. To further investigate the applicability of the new method, an experimental study was conducted on a steel simplified scale model of a monopile offshore wind turbine support structure. The results demonstrate that the proposed signal-segments cross-coherence method and the new damage localization index can be used to detect the bolt-loosening damage of the steel structure only with output signals.