Vibration-based Damage Detection Research Articles

Model-Based Damage Localization Using the Particle Swarm Optimization Algorithm and Dynamic Time Wrapping for Pattern Recreation.

Vibration-based damage detection methods are a subcategory of Structural Health Monitoring (SHM) methods that rely on the fact that structural damage will affect the dynamic characteristic of a structure. The presented methodology uses Finite Element Models coupled with a metaheuristic optimization algorithm in order to locate the damage in a structure. The search domains of the optimization algorithm are the variables that control a parametric area, which is inserted into the FE model. During the optimization procedure, this area changes location, stiffness, and mass to simulate the effect of the physical damage. The final output is a damaged FE model which can approximate the dynamic response of the damaged structure and indicate the damaged area. For the current implementation of this Damage Detection Framework, the Particle Swarm Optimization algorithm is used. As an effective metric of the comparison between the FE model and the experimental structure, Transmittance Functions (TF) are used that require output only acceleration signals. As with most model-based methods, a common concern is the modeling error and how this can be surpassed. For this reason, the Dynamic Time Wrapping (DTW) algorithm is applied. When damage occurs in a structure it creates some differences between the Transmittance Functions (TF) of the healthy and the damaged state. With the use of DTW, the damaged pattern is recreated around the TF of the FE model, while creating the same differences and, thus, minimizing the modeling error. The effectiveness of the proposed methodology is tested on a small truss structure that consists of Carbon-Fiber Reinforced Polymer (CFRP) filament wound beams and aluminum connectors, where four cases are examined with the damage to be located on the composite material.

Structural Damage Identification Methods in Truss Bridge Structures Using Vibration Analysis: A Review

Developing countries will always engage in infrastructure development in various regions, and one notable aspect of this development is the construction of steel frame bridges. Bridges are complex structures with a myriad of challenges. The increasing number of cases of steel frame bridge collapses has prompted humans to become more conscious of Structural Health Monitoring (SHM) activities. In order to implement this, the development of a straightforward structural damage detection method has been pursued, suitable for both simple and highly complex structures, commonly referred to as Vibration-Based Damage Detection (VBDD). Various algorithms have been proposed to achieve the goal of identifying structural damage, enabling prompt and accurate decision-making in handling such situations. This article delves into the discussion of several proposed algorithms for achieving this objective.

Vibration based damage detection of bridge structures

The task of identifying damage to the load-bearing beams of large-span bridge structures using vibration diagnostic methods has been implemented, taking into account experimental uncertainties associated with natural phenomena and technological processes. The methodology for localizing damage is based on the analysis of dynamic characteristics of the structure, such as natural frequencies and modes of vibration. The study presents research on a finite element model of a bridge structure using a method for determining a damage indicator based on normalizing the values of vibration modes with respect to the values of vibration area.

Impact of the model error on the neural network-based damage detection

Machine Learning (ML) techniques applied to vibration-based damage detection of structures showed promising results in identifying damage for their capability of feature discrimination even in presence of noise-corrupted data. In this context, the paper presents the application of a damage detection procedure based on neural networks to a railway bridge and proposes a procedure to take into account for the model error in the training phase. The output of the network, that depends on the dynamic features given as input, allows to classify the structure state as undamaged, lightly damaged or severely damaged. The procedure employs only simulated data but includes a series of expedients to approach a real situation, like the stochastic modelling of measurement errors and the use of two different models to account for the model error. The performances of the networks are analyzed with respect to datasets generated by the two different models. The results show the relevance of accounting for model error in the calibration of the network to obtain a robust damage identification.

Experimental Investigation on Damage Sensitive Dynamic Responses of a Reinforced Concrete Beam

Reinforced concrete structures are subjected to damage due to environmental loading and operational conditions. Early detection of damages in reinforced concrete structures is very important. However, this becomes difficult because failure at the micro level in the form of minute cracks, develops much earlier than the visual appearance of actual damages resulting out of coalesce of several such micro level damages. Vibration based damage detection techniques, particularly use of modal testing by exciting a structure dynamically and measuring resulting responses is well established in current practice. Still, real experimental investigation involving a full scale reinforced concrete structure is somewhat rare in current literature. In the present investigation, dynamic responses of a 3.3-metre long reinforced concrete beam were measured experimentally before and after damage. Damage in the form of flexural cracks was inflicted by applying quasi-static load using a universal testing machine of capacity 300 kN under the four-point bending configuration. Broadband roving impact excitation was imparted through an impact hammer and the resulting responses were picked up by a single accelerometer. The time signals of both the force and acceleration responses were Fourier transformed using a spectrum analyser to determine the frequency response functions. The modal parameters e.g. frequencies, mode shapes, modal damping factors are found out through curve fitting. The frequency response function at a particular point has also been investigated for all the load increments and gradual changes into the dynamic properties are noted. Comparison of modal parameters between the undamaged and damaged state including their curvatures indicates that they are sensitive to the crack locations. On the other hand, the differences in frequency response functions including their curvatures were sensitive to the damage intensity in turn. The current experimental investigation provides great insight into the application of vibration-based damage detection technique to reinforced concrete structures.

Vibration-Based Damage Detection on Railway Wheel Tires

Die Verkehrswende bedingt einen Ausbau des Schienen-Personenverkehrs. Eine in situ Schadenserkennung während des Betriebs kann einen Beitrag zur Kapazitätserhöhung und Sicherheit im Schienenverkehr liefern. Im Vortrag werden Forschungen bezüglich einer schwingungsbasierten, zerstörungsfreien Methode zur Detektion von Radreifenschäden vorgestellt. Über eine Transferpfadanalyse (TPA) wird auf den Körperschall jedes Rades während der Überfahrt rückberechnet. Mit dem synthetisierten Körperschall wird mittels des akustischen Resonanztests die Schadensidentifikation durchgeführt.

A kernel canonical correlation analysis approach for removing environmental and operational variations for structural damage identification

Vibration-based damage detection relies on the observation of changes in damage-sensitive dynamic features. However, a major problem is that dynamic features are sensitive not only to structural damage but also to environmental and operational variations (EOVs), such as temperature, humidity, and operational loading. In addition, the influence of EOVs on damage-sensitive features is often nonlinear, which limits the application of many linear methods in the removal of environmental effects. To remove the nonlinear effects of EOVs on dynamic features, an improved method based on kernel canonical correlation analysis (KCCA) is proposed in this study. Using this method, the monitored data were divided into two groups. The two sets of data were then mapped into a higher-dimensional space through the kernel trick to determine their implicit linear relationship. Subsequently, two variables that share the co-occurrence information of EOV effects were computed using canonical correlation analysis (CCA), and a stationary residual insensitive to EOVs was obtained. Furthermore, the proposed approach was examined using a simulated 7-DOF example and then applied to real monitored data from the Z24 bridge, demonstrating that nonlinear EOV effects can be successfully removed and damage can be accurately identified.

Vibration-based structural damage detection using 1-D convolutional neural network and transfer learning

This paper presents a novel vibration-based structural damage detection approach by using a one-dimensional convolutional neural network (1-D CNN) and transfer learning (TL). The CNN can effectively extract structural damage information from the vibration signals. However, the CNN training needs enough samples, while some damage samples (scenarios) obtained from real structures are limited, which will compromise the CNN ability to detect structural damage. As a solution, the numerical models have potential to provide sufficient CNN training samples; meanwhile, the state-of-the-art TL technique can significantly shorten the network training time and improve the accuracy. Therefore, this paper proposes a new method to detect the damage of a bridge model. The 1-D CNN is firstly trained with the samples of the single damage scenarios of the numerical bridge model. And then it is transferred to the complex scenarios of multi-damage (double or triple simultaneously), random size structures, and experimental model. The results demonstrate that: with the TL, the accuracy of damage detection is increased by about 47% at most, and the convergence speed is increased by at least 50%; in particular, the TL can inhibit over-fitting, and for the real bridge case, the accuracy also increased by 44.4%. It is demonstrated that: the TL can effectively improve the damage detection accuracy and convergence effect, and the application of this method to the random size structures also proves its generalization.

A fully blind and adaptive filter method to solve the inverse problem in vibration-based gear damage detection

In vibration-based gear damage detection, the fault signatures are usually corrupted by noise and convolution of vibration transmission channels. Deconvolution techniques have been widely studied to solve the inverse problem for restoring fault signatures. The mainstream methods, however, often fail due to the requirement of prior knowledge about fault signatures or the experience-based determination of filter parameters. In this paper, a fully blind and adaptive filter method is proposed. Specifically, a new criterion is defined, and the filter method is formulated as a maximization problem based on the criterion. The detailed deduction and pseudo-code for iteratively solving the problem are presented. Moreover, the filter method embeds an advanced swarm intelligence algorithm—that is, the grey wolf optimizer to achieve the adaptive determination of the optimal filter parameters according to the signals. In this context, it is a fully blind and adaptive filter method without requiring any prior knowledge and experiences about the target signatures to pre-determine filter parameters. Analysis results of a simulated signal and the comparisons with several state-of-the-art methods show the effectiveness of the proposed method for restoring the repetitive fault impulses from the mixed signal containing complex interferences. Fault-injection experiment and engineering application are carried out and the results demonstrate the performance of the proposed method for solving the inverse problem in gear damage detection.

A Hybrid Method for Vibration-Based Bridge Damage Detection

Damage detection algorithms employing the conventional acceleration measurements and the associated modal features may underperform due to the limited number of sensors used in the monitoring and the smoothing effect of spline functions used to increase the spatial resolution. The effectiveness of such algorithms could be increased if a more accurate estimate of mode shapes were provided. This study presents a hybrid structural health monitoring method for vibration-based damage detection of bridge-type structures. The proposed method is based on the fusion of data from conventional accelerometers and computer vision-based measurements. The most commonly used mode shape-based damage measures, namely, the mode shape curvature method, the modal strain energy method, and the modal flexibility method, are used for damage detection. The accuracy of these parameters used together with the conventional sparse sensor setups and the proposed hybrid approach is investigated in numerical case studies, with damage scenarios simulated on a simply-supported bridge. The simulations involve measuring the acceleration response of the bridge to ambient vibrations and train crossings and then processing the data to determine the modal frequencies and mode shapes. The efficiency and accuracy of the proposed hybrid health monitoring methodology are demonstrated in case studies involving scenarios in which conventional acceleration measurements fail to detect and locate damage. The robustness of the proposed method against various levels of noise is shown as well. In the studies considered, damage as small as 10% decrease in flexural stiffness of the bridge and localized in less than 1% of the span-length of the bridge is reliably detected even with very high levels of measurement noise. Finally, a modified modal flexibility damage parameter is derived and used to alleviate the shortcomings of the modal flexibility damage parameter.

Study on the dynamic behaviour of Bafgh-Bandar Abbas lane railway bridge for structural health monitoring purpose

ABSTRACT This paper uses the vibration-based damage detection method for structural health monitoring of a Railway Bridge. Hence, the bridge had been exactly modelled in finite element analysis software. Due to the elastomeric bridge bearing, direct calculating of the natural frequencies and extracting the mode shapes of the bridge is not suitable and effective. Therefore, a time-dependent transient analysis of the train movement on the bridge is done and then the vibrations of all truss cells during the train crossing period were extracted. Afterwards, the relevant data is transformed, which represents the natural frequencies of the bridge vibration and their amplitude at each point. In the following, the mode shapes of vibration and the distribution of vibration energy are calculated. Applying any artificial damages in the model, occurring changes in the natural frequencies, mode shapes, and the vibration energies of the modes, are examined. Consequently, detecting and locating damages in the structure has been done with acceptable accuracy. Finally, for validation of the results, an accelerometer is installed on the bridge truss in the middle part of the bridge to extract the acceleration of bridge vibration at the train crossing time interval. In the end, the results are compared and presented.

Learning-based video motion magnification approach for vibration-based damage detection

This paper explores the viability of using learning-based state-of-the-art video motion magnification to extract vibrational signatures for damage detection in structures. Unlike previous research, the proposed model uses learning-based video motion magnification rather than implementing hand-designed filters. This change allows the presented approach to detect more subtle sub-pixel movement and thus allows for greater sensibility to vibration. This novel approach is validated and verified on a laboratory structural benchmark under different damage scenarios. Although the learning-based model was trained on a synthetic and non-related image dataset, the experimental results prove that the system is suitable for identifying natural frequencies and operating deflection shapes, thus enabling damage detection algorithms to identify structural damage reliably. The results demonstrate the feasibility and suitability of this novel monitoring technique and thus open an avenue for further research regarding deep learning and its applications to structural health monitoring.

Gibbs Sampler-Based Probabilistic Damage Detection of Structures Using Reduced Order Model

Vibration-based global damage detection based on updating of finite element (FE) model by targeting the modal measurements is a significant area of interest in structural health monitoring (SHM). In a typical modal testing setup, the measured mode shapes have missing components against various degrees of freedom (DOFs) due to the limitation in the number of sensors available. In this context, a novel Gibbs sampling approach is proposed for updating of FE model incorporating model reduction (MR) to facilitate the global-level detection of structural damages from incomplete modal measurements. In addition to the ease with similar sizes of analytical and experimental mode shapes, the proposed Gibbs sampling approach (for updating the reduced order FE model in the Bayesian framework) has some important advantages like: (A) no need for consideration of system mode shapes as parameters (unlike needed in the typical Gibbs sampling approach) thereby having a significant reduction in the number of parameters, (B) non-requirement of mode matching with consequent reduction in computation time to a significant extent. A generalized formulation is presented in this work providing the scope for incorporating measurements from multiple sensor setups. Moreover, formulations are adapted to incorporate multiple sets of data/measurements from each setup targeting the epistemic uncertainty. Finally, validation is carried out with both numerical (truss structure and building structure) and experimental (laboratory building structure) exercises in comparison with the typical Gibbs sampling approach having a full-sized model. The proposed approach is observed to be evolved as a computationally efficient technique with satisfactory performance in FE model updating and global damage detection.

A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model.

Axially loaded beam-like structures represent a challenging case study for unsupervised learning vibration-based damage detection. Under real environmental and operational conditions, changes in axial load cause changes in the characteristics of the dynamic response that are significantly greater than those due to damage at an early stage. In previous works, the authors proposed the adoption of a multivariate damage feature composed of eigenfrequencies of multiple vibration modes. Successful results were obtained by framing the problem of damage detection as that of unsupervised outlier detection, adopting the well-known Mahalanobis squared distance (MSD) to define an effective damage index. Starting from these promising results, a novel approach based on unsupervised learning data clustering is proposed in this work, which increases the sensitivity to damage and significantly reduces the uncertainty associated with the results, allowing for earlier damage detection. The novel approach, which is based on Gaussian mixture model, is compared with the benchmark one based on the MSD, under the effects of an uncontrolled environment and, most importantly, in the presence of real damage due to corrosion.

Nonlinear vibration analysis of beam-like bridges with multiple breathing cracks under moving vehicle load

The beam-like bridges with small and medium spans are designed to mainly sustain the moving vehicle. Hence, the dynamic behavior of bridges under vehicular loading can provide important information for the bridge design, assessment, and maintenance. Cracks, as one of the most common damage in the bridge structures, alter the bridge dynamic characteristics and therefore responses to the moving vehicle. This paper proposes a novel semi-analytical approach for nonlinear vibration analysis of the beam-like bridges with breathing cracks subjected to vehicular loading, taking into account the dynamic interactions between the vehicle and cracked bridge. The Dirac’s delta function is utilized to represent the local stiffness change at the crack location, leading to closed-form solutions of mode shapes of the cracked beam. The breathing process of the crack, causing eigenproperties of the beam to change over time, is characterized by the time-varying curvature of the bridge. Subsequently, the nonlinear dynamic equation of the vehicle-cracked bridge interaction system is established, which is solved through a formulated double-iterative numerical scheme. Eigenvalues and dynamic responses of the bridge with open crack presented in the literature are compared with those obtained from the proposed approach for purposes of validation. The effects of various crack parameters, vehicle speed, and bridge surface irregularity are investigated through a parametric study. In addition, the detection of breathing cracks is also discussed. The results show that the proposed approach can be used to effectively obtain the nonlinear vibration characteristics and dynamic responses of the beam-like bridge with general boundary conditions and different crack numbers, locations, depths, and initial status under moving vehicle. Moreover, the approach has the potential to be incorporated into the vibration-based bridge damage detection.

Generative adversarial networks for labeled acceleration data augmentation for structural damage detection

There have been major advances in the field of data science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have been utilized for civil structural health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training data set of a 1-D deep convolutional neural network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures.

Structural damage detection based on fundamental Bayesian two-stage model considering the modal parameters uncertainty

In structural health monitoring (SHM), damage detection is a final target to know the real status of the objective structure. Vibration-based damage detection is a commonly used method, since it makes full use of the dynamic characteristics. Improving the efficiency of this kind of methods has attracted increasing attentions. The existing uncertainty of identified modal parameters using measured data may significantly affect the detection accuracy. Furthermore, an optimization algorithm with a better convergence speed can improve the detection accuracy and reduce the computational time. This article presents the work to develop a novel damage detection method based on fundamental Bayesian two-stage model and sparse regularization. In this method, the most probable value of modal parameters and the associated posterior uncertainty are combined to investigate the effect of uncertainty on damage detection. The usage of the sparse regularization in the objective function can decrease the complexity of modeling and avoid the overfitting problem. A machine learning method combining intelligent swarm optimization algorithm with K-means clustering was used to carry out the optimization. Finally, a method combining three existing theory, that is, fundamental Bayesian two-stage model, sparse regularization, and I-Jaya algorithm, was developed. To investigate the efficiency of the proposed method, the traditional objective functions with and without the sparse regularization were also used for the comparison. The proposed method was verified by an ASCE benchmark example, and then it is applied into an experimental structure. The results show that due to the consideration of uncertainty, the objective function based on the fundamental Bayesian model and sparse regularization has a better performance.

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.

Vibration-Based Damage Detection of Arch Dams Using Least-Square Support Vector Machines and Salp Swarm Algorithms

Vibration-Based Damage Detection of Arch Dams Using Least-Square Support Vector Machines and Salp Swarm Algorithms

Structure-type classification and flexibility-based detection of earthquake-induced damage in full-scale RC buildings

Detecting early damage in civil structures is highly desirable. In the area of vibration-based damage detection, modal flexibility (MF)-based methods have proven to be promising tools for promptly identifying changes in the global structural behavior. Many of these methods have been developed for specific types of structures, giving rise to different approaches and damage-sensitive features (DSFs). Although structural type classification is an important part of the damage detection process, this part of the process has received little attention in most literature and often relies on the use of a-priori engineering knowledge. Moreover, in general, experimental validations are only performed on small-scale laboratory structures with controlled artificial damage (e.g., imposed stiffness reductions). This paper proposes data-driven criteria for structure-type classification usable in the framework of MF-based damage identification methods to select the most appropriate algorithms and DSFs for detecting and localizing structural anomalies. This paper also tests the applicability of the proposed classification criteria and the damage identification methods on full-scale reinforced concrete (RC) structures that have experienced earthquake-induced damage. The considered structures are a seven-story RC wall building and a five-story RC frame building, which were both tested on the large-scale University of California, San Diego-Network for Earthquake Engineering Simulation (UCSD-NEES) shaking table.