Damage Identification Approach Research Articles

Identification of structural damage using S-transform from 1D and 2D mode shapes

An improvement of vibration-based damage identification in structures attracted much attention over the last decades. Many of the developed algorithms to-date, like those based on wavelet transforms (WTs), reveal high sensitivity to damage, however suffer from numerous drawbacks, which include significant dependency of a damage detectability on selection of a basis function or the boundary effect. This paper presents a novel alternative approach for damage identification in beams and plates based on the S-transform, which combines numerous advantages of the short-time Fourier transform and an ability of multiresolution analysis of WTs. The efficiency of the proposed approach was tested and validated on 1D and 2D mode shapes of various composite structures and various types of damage obtained both from numerical simulations and experimental studies. The obtained results confirmed high sensitivity of the approach based on the S-transform to structural damage as well as revealed full reduction of the boundary effect.

Sensor clustering-based approach for structural damage identification under ambient vibration

This study explored the sensor clustering-based damage detection beyond the free-vibration limitation to allow for the direct utilisation of time-series for damage identification under ambient vibration. In the proposed method, a dense sensor network is clustered and each sensor cluster is represented by nonlinear autoregressive with exogenous inputs (NARX) model, which is developed in a black-box manner via an artificial neural network. Damage detection is achieved through a new damage sensitive feature which is formulated from the NARX neural network prediction error. The efficiency of the proposed methodology is assessed firstly using test data of an 8-DOF system and later by conducting an experimental study on scaled steel arch laboratory models subjected to various damage cases. The obtained results reveal that the proposed method can satisfactorily detect, localise, and estimate damage severity in the test structure.

Metamodel-based pattern recognition approach for real-time identification of earthquake-induced damage in historic masonry structures

Damage localization/quantification through vibration-based Structural Health Monitoring (SHM) is commonly performed by inverse calibration of a numerical model. Nevertheless, the numerous simulations required in the associated optimization problem pose a daunting obstacle when applied to real-time SHM. Particularly critical are heritage buildings, whose complex geometries often require computationally intensive modellings. In this light, this paper presents a novel earthquake-induced damage identification approach for historic masonry structures. This relies upon the use of a computationally efficient meta-model suited for real-time system identification. The optimization problem is formulated accounting for discrepancies between numerical and experimental resonant frequencies and mode shapes. Damage localization/quantification is enabled by multivariate analyses of continuously identified model parameters. A real medieval tower is presented as a case study, and several damage scenarios are simulated and used for validation. The reported results pave the way for the development of next-generation long-term vibration-based SHM systems with real-time damage identification capabilities.

Tuning metaheuristic algorithms using mixture design: Application of sunflower optimization for structural damage identification

This paper presents an efficient inverse global optimization approach for damage identification of plate-like structures. In this approach, the damage identification process is performed by minimizing an objective function based on modal parameters of CFRP laminated structures. The identification process entails two steps: i) the direct problem is modeled using the finite element method. Damage is induced into the two different situations, first as a variation in physical properties, i.e., delamination, as a variation in stiffness and also as a variation in the grommet properties, for example small circular holes; ii) For solving the optimization problem, an enhanced SunFlower Optimization (SFO) algorithm is applied in the inverse problem methodology. The SFO metaheuristic algorithm has its biological operators optimized by mixture design method. The efficiency of the proposed identification is investigated through two numerical examples for laminated composite plates where Genetic Algorithm, SFO and an improved SFO algorithm are compared. The obtained results indicate that the proposed Structural Health Monitoring method can successfully identify the location and the severity of small induced damage cases in the laminated composite plate. In addition, the improved algorithm was shown to be more efficient and accurate than the widely known and applied Genetic Algorithm.

Baseline-free damage identification based on asymmetrical energy consumption

In order to solve the problem of obtaining accurate data for an intact beam, a baseline-free damage identification approach, based on the difference in the energy consumption of a beam, has been presented in this paper. An energy model was established in order to illustrate that the difference in the energy consumption is mainly due to the respiration effect of cracks, and that the energy consumption of a beam bending downward can be utilized as a replacement for the baseline data. Thus, the standard data and the comparative data can be separated from the measurement data. Based on this data, a statistical damage factor that can be used to locate and quantify the damage in a beam has been defined. Finally, an identification algorithm was established and has been experimentally verified for use with pre-damaged reinforced concrete beams. The experimental results have illustrated that the location and singularity of a singular point in the damage indicator sequence can locate the damage and quantify the severity of the damage in a beam, respectively.

An efficient two-step damage identification method using sunflower optimization algorithm and mode shape curvature (MSDBI–SFO)

Laminated composite structures performance and behavior can be affected by damage that is not always visible on the surface. The need to monitor the health of these structures has continuously increased, which can be achieved in a fast and cost-effective way by numerical simulations. This paper presents an efficient two-step approach for damage identification in laminated composite plates. The first step uses mode shape and its derivatives (mode shape curvature) to locate the damages based on modal data. The proposed indicator utilizes modal analysis information extracted from finite element analysis. Then, a new metaheuristic Sunflower Optimization method (SFO) is employed to assess the correct severity of induced damages. This technique considers the damage detection problem as an inverse problem with minimization of an objective function. Numerical examples considering laminated composite plate with one and two induced damaged sites (delamination) are considered. The results indicate that the proposed method not only successfully identifies the location and severity of multi-damage cases in the composite structures, but also provides a better efficiency in terms of time saving and computational costs.

Non-probabilistic method to consider uncertainties in structural damage identification based on Hybrid Jaya and Tree Seeds Algorithm

This paper proposes a novel non-probabilistic structural damage identification approach by developing a hybrid swarm intelligence technique based on Jaya and Tree Seeds Algorithm (TSA), taking into account the high-level uncertainties in the measurements and finite element modelling. The damage in structure is simulated as reduction of elemental stiffness, and structural damage identification is formulated as an optimization problem. To overcome the challenge for structural damage identification with a limited number of measurement data, an objective function based on the modal data and sparse regularization technique is defined. To make the optimization algorithm more powerful and robust, a hybridization of the K-means clustering based Jaya and TSA is proposed. Jaya algorithm is taken as the core in the hybridization. The clustering strategy is employed to replace solutions with low-quality objective values in the Jaya algorithm. Then the search strategy of the TSA is introduced into the best-so-far solution of each cycle. The proposed hybridization algorithm is termed as “ C-Jaya-TSA”. To enhance the capacity of the proposed algorithm to consider uncertainties, a non-probabilistic method is also integrated to calculate the interval bound (lower and upper bounds) of the elemental stiffness changes by using the interval analysis method. To better quantify the structural damage extents, Damage Measure Index (DMI) values are introduced for representing structural damage states. The DMI value can be viewed as a combination of deterministic stiffness reduction and the Possibility of Damage Existence (PoDE). Numerical benchmark functions, numerical studies and experimental investigations are conducted to verify the accuracy and performance of the proposed method. The identification results show that the developed C-Jaya-TSA integrated with the non-probabilistic interval analysis method is a promising tool to accurately identify the structural damage, even high-level uncertainties exist.

Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

A Bayesian probabilistic approach for damage identification in plate structures using responses at vibration nodes

Structural health monitoring for plate structures is important since they are essential structural components in many applications. One interesting topic for structural health monitoring methods is to achieve accurate damage detection with a small number of sensors and without the requirement of a high-fidelity finite element model. Traditional damage detection techniques for plates need many sensors to be distributed on the surface of the plate. This paper adopts dynamic responses at a few vibration nodal points combined with a Bayesian probabilistic approach for damage identification in plate structures. Vibrational amplitudes at nodal points, also referred as node displacement or NODIS, have the potential to achieve real-time damage assessment with a relatively small number of sensors. Thus, they can serve as efficient structural damage indicators. Despite these advantages, this method has not been applied to plate-type structures. This paper proposes a vibration-based SHM method for plates that is suitable for real-time monitoring, requires a small number of industrial sensors, does not rely on a high-fidelity FE model and can be applied for damage assessment of location and severity. In the Bayesian framework, an efficient perturbation-based surrogate model is derived for plate structures to replace the expensive FE model. The accuracy of the perturbation-based surrogate model is investigated and compared with FE results. Then, this paper evaluates the performance of the NODIS-based Bayesian framework with the perturbation method by comparing it with FE results. At last, the proposed method is applied to a carbon fiber reinforced polymer sandwich structure with different grinding depths.

An Effective Approach for Damage Identification in Beam-Like Structures Based on Modal Flexibility Curvature and Particle Swarm Optimization

In this paper, a computationally simple approach for damage localization and quantification in beam-like structures is proposed. This method is in consonance with applying modal flexibility curvature (MFC) and particle swarm optimization (PSO) algorithm. Analytical studies in the literature have revealed that changes in the modal flexibility curvature can be considered as a sensitive and suitable criterion for identifying damage in the beam-like structures. Modal flexibility curvature can be calculated utilizing central difference approximation, based on entries of the modal flexibility matrix. The PSO algorithm, as a powerful optimization tool, is employed in order to minimize the error function which is formulated as an error function between the measured modal flexibility curvatures of the damaged structure and those calculated from the analytical structure. To demonstrate the efficiency of the method, two beam-like structures under different damage scenarios are studied. In addition, the robustness of presented method is investigated only when the first several modal data are available. It is observed that the proposed approach is able to localize and quantify various damage cases only by a few lower vibrational modes and also, it is low-sensitive to measurement noise.

A new adaptive approach of the Metropolis-Hastings algorithm applied to structural damage identification using time domain data

In the present work, the formulation and solution of the inverse problem of structural damage identification is presented based on the Bayesian inference, a powerful approach that has been widely used for the formulation of inverse problems in a statistical framework. The structural damage is continuously described by a cohesion field, which is spatially discretized by the finite element method, and the solution of the inverse problem of damage identification, from the Bayesian point of view, is the posterior probability densities of the nodal cohesion parameters. In this approach, prior information about the parameters of interest and the quantification of the uncertainties related to the magnitudes measured can be used to estimate the sought parameters. Markov Chain Monte Carlo (MCMC) method, implemented via the Metropolis-Hastings (MH) algorithm, is commonly used to sample such densities. However, the conventional MH algorithm may present some difficulties, for instance, in high dimensional problems or when the parameters of interest are highly correlated or the posterior probability density is very peaked. In order to overcome these difficulties, a new adaptive MH algorithm (P-AMH) is proposed in the present work. Numerical results related to an inverse problem of damage identification in a simply supported Euler-Bernoulli beam are presented. Synthetic experimental time domain data, obtained with different damage scenarios, and noise levels, were addressed with the aim at assessing the proposed damage identification approach. An adaptive MH algorithm (H-AMH) and the conventional MH algorithm, already consolidated in the literature, were also considered for comparison purposes. The numerical results show that both adaptive algorithms outperformed the conventional MH. Besides, the P-AMH provided Markov chains with faster convergence and better mixing than the ones provided by the H-AMH.

A fast friction-model-inspired sparse regularization approach for damage identification with modal data

In this paper, a new and fast friction-model-inspired sparse regularization approach has been proposed for structural damage identification. The proposed approach is motivated by the fact that the sparse regularization can be revisited as a friction model with the regularization parameter being the static threshold of the friction forces. Based on the friction model, it is derived that the sparsity of the solution can be easily accomplished by immediately setting the regularization parameter as one of the friction forces; this avoids the costly trial selection of the regularization parameter in the existed L-curve method. Numerical examples and an experimental test case are studied and final results reveal that the proposed approach is rather accurate and robust in terms of damage identification performance and it is also much more efficient than the conventional L-curve based sparse regularization approach.

Structural damage identification based on short-time temporal coherence using free-vibration response signals

This paper proposes a short-time temporal coherence (STC)-based approach for linear and nonlinear structural damage identification. Two novel damage indices (damage detection index, DDI and damage localization index, DLI) are constructed based on the peak coherence function of the STC, which aims at detecting and localizing the damage using the measured free-vibration responses. Detailed numerical investigation was performed on a spring-damping-mass system. The results show that the two indices are sensitive to linear and nonlinear structural damage, and they can accurately identify the damage even with severely noise-polluted signals. An experimental study was conducted on a steel cantilever column to further validate the proposed approach. A typical nonlinear structural damage was simulated by loosening the bolts in the flange connection, which was successfully identified by the two indices. The results demonstrate that the proposed STC-based damage identification approach is effective to identify the bolt-loosening of structure with free-vibration response signals.

Data mining-based damage identification of a slab-on-girder bridge using inverse analysis

Classical damage detection methods such as visual inspections have many limitations, i.e. time consuming procedure, costly process and ineffective for large and complex structural systems. To overcome these difficulties, a data mining-based damage identification approach is developed in this study. First four natural frequencies which obtained from the experimental modal analysis of a slab-on-girder bridge structure are used as an input database. The laboratory work is carried out through single-type and multiple-type damage scenarios. The applicability of machine learning, artificial intelligence and statistical data mining techniques are here examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to predict the model behavior and damage severity. Then, a hybrid algorithm is proposed in the deployment step of Cross Industry Standard Process for Data Mining (CRISP-DM) model. According to the obtained results, the hybrid algorithm performs a better accuracy in compare to ANN technique itself.

Damage identification from static tests by eigenparameter decomposition and sparse regularization

Identifying the damages from test data is central to assuring the structural safety. The static model is the simplest model to describe the mechanical behavior of the structure where only the stiffness is involved and it is independent of the mass and the complex damping. As a result, damage identification based on the static data will not be deteriorated by the inexact damping and the possible error in the mass. Notwithstanding, the major difficulty regarding damage identification with static test data is that the amount of the static data is quite limited and insufficient with respect to the amount of damage parameters, rendering the identification very sensitive to the measurement noise. Attempting to circumvent this difficulty, a novel damage identification approach is developed in this article where the sparse regularization is introduced to implicitly enforce the sparsity constraint of the damage locations. Moreover, in order to work well with the sparse regularization, a new goal function is established by resorting to the eigenparameter decomposition for which the decoupling feature would make the sparse regularization be tackled immediately with closed-form solutions. Then, the alternating minimization approach is used to get the solution of the new goal function and the threshold setting method is simply called to determine a proper regularization parameter. Numerical and experimental examples are studied to testify the feasibility, accuracy, and robustness of the proposed damage identification approach.

An Innovative Methodology for Online Surrogate-Based Model Updating of Historic Buildings Using Monitoring Data

ABSTRACT Structural Health Monitoring (SHM) based on Automated Operational Modal Analysis (A-OMA) has gained increasing importance in the conservation of heritage structures over recent decades. In this context, finite element model updating techniques using modal data constitute a commonly used approach for damage identification. Nevertheless, the large number of simulations usually involved in the associated minimization problem hinders the application to real-time condition assessment. This is especially critical for historic buildings, where the modelling of complex geometries involves large computational burdens. Alternatively, surrogate models offer an efficient solution to replace computationally demanding numerical models and so perform continuous model updating. In this light, this paper presents a surrogate-based model updating approach for online assessment of historic buildings and its application to a medieval masonry tower, the Sciri Tower in Perugia (Italy). Using modal properties identified by A-OMA, the proposed approach allows the continuous fitting of certain damage-sensitive parameters of the structure. To do so, three different surrogate models are considered, including the quadratic response surface method, Kriging, and Random Sampling High-Dimensional Model Representation, and their effectiveness is compared from an SHM perspective. The reported results demonstrate the suitability of the proposed methodology for tracking the temperature-dependent intrinsic properties of the tower.

A Shannon entropy approach for structural damage identification based on self-powered sensor data

Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions.

Practical Residual Force Decomposition Method for Damage Identification of Existing Reticulated Shells

The non-destructive damage identification approaches for existing structures become more of a concern due to safety requirements. However, the application of damage identification method is still limited for complex spatial structures. In this paper, a practical residual force decomposition (RFD) method is proposed to identify damaged members in existing reticulated shell structures. A parameterized model is built by taking the stiffness reduction into consideration. In the proposed RFD method, the significant damage can be recognized corresponding to the changes of the observed eigenvector of the reticulated shells, and the eigenvector changes can be easily transferred to a set of multivariate linear equations to locate the members with significant stiffness reduction. The inputs of the equations are discussed including the additional constraints and the selection of effective modes. According to the different proportion of the numbers of members and joints, three situations of the solution are discussed. Especially when the solution is not unique, the importance coefficients of structural members are introduced as additional constraints through Ritz vector sensitivity analysis and application of the Modal Assurance Criterion. Two illustrative examples including a single-layer Kiewitt-6 reticulated shell and a structural model of international convention center are adopted to demonstrate the effectiveness of the approach. The results show that the proposed method can yield a suitable damage identification.

A nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics

In this study, a nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics is proposed for uncertainty-oriented damage identification with insufficient parametric information. A deterministic damage identification method is firstly reviewed. By means of a nonnegative least squares algorithm that combines truncated singular value decomposition and the L-curve method, the ill-posed damage equation is then solved, and the damage index is simultaneously obtained. In terms of the uncertainty quantification issue, the material elastic modulus and density in structural models herein are described as unknown-but-bounded interval numbers. On the basis of orthogonal polynomial expansion and interval mathematics, the set collocation methodology is presented to determine the upper and lower bounds of the damage index. The interval bounds can provide supports for structural health diagnosis under uncertain conditions. A nonprobabilistic identification using the first-order Taylor series expansion is also proposed for comparison's purpose. Two numerical applications and one test are finally given under single damage and multidamage scenarios with different uncertainty degree. Results suggest that the presented nonprobabilistic structural damage identification approach can identify the damage location and degree with consideration of uncertainties.

Baseline updating method for structural damage identification using modal residual force and grey wolf optimization

ABSTRACTThis article presents two effective optimization-based model-updating approaches for structural damage identification using the modal residual force (MRF) concept. The first objective function employs a direct data-fitting procedure to inspect the amount of approaching between entries of the calculated MRF vectors for the monitored and analytical models of the structure. The second objective function uses the modal assurance criterion as a geometric tracing criterion to evaluate the amount of accordance between two vectors. The proposed objective functions are solved with a novel metaheuristic optimization technique, the grey wolf optimization algorithm. Four numerical examples are studied to demonstrate the efficiency of the proposed methods. Moreover, studies are conducted not only to assess the robustness of the methods in more realistic circumstances, but also to justify the necessity of using these techniques. Based on the obtained results, the second objective function is more sensitive to structural damage than the first objective function.