Damage Identification Problem Research Articles

A Comparative Study on Inverse Vibration Based Damage Assessment Techniques in Beam Structure Using Ant Colony Optimization and Particle Swarm Optimization

Use of swarm intelligence has proliferated over previous couple of years for damage assessment in large and complex structures using vibration data. Available literatures shows ‘ant colony optimization’ (ACO) and ‘particle swarm optimization’ (PSO) are predominantly used for solving complex engineering problems including damage identification and quantification problems. The time requirement and accuracy of the vibration based damage identification algorithms depends on early exploration and late exploitation capabilities of soft computing techniques. However, there are not any literature available comparing algorithms on these bases. In the current study, an inverse problem is constructed using the natural frequency changes which is then solved using ACO and PSO algorithms. The algorithm is run for identification of single and multiple damages in simple support and cantilever beam structures. It's found that, both ACO and PSO based algorithms are capable of detecting and quantifying the damage accurately within the limited number of iterations. However, ACO based algorithm by virtue of its good exploration capability is able to identify near optimal region faster than PSO based algorithm, whereas PSO algorithm has good exploitation capability and hence able to provide better damage quantification than ACO algorithm at latter stages of iteration. Further, PSO based algorithm takes less time to reach at required accuracy level. It is also observed that, the time required for these algorithms are independent of numbers of damage and support conditions.

An Improved Multiple Damage Identification Method of Plate Structure Using Discrete Wavelet Transform

In order to improve the identification accuracy of multiple damage of the plate structures, a method based on a two-dimensional discrete wavelet transform is proposed. Firstly, the structural characteristic parameters of the undamaged and damaged plate structures are obtained through the finite element modal analysis. The discrete wavelet transform is used to decompose and evaluate the modal node displacement data of the rectangular thin plate, and the high frequency information and singular values of the structural damage location are calculated. Secondly, the quantitative analysis of damage is realized by the change rate of the maximum value of the wavelet coefficient. Finally, according to the proposed algorithm, the anti-noise performance of the algorithm is verified through different ratios of noise. The results are shown that the vibration-based damage identification method is effective to detect the location and severity of the rectangular thin plate structures, which can be used to solve the damage identification problems of complex structures.

Lichtenberg optimization algorithm applied to crack tip identification in thin plate-like structures

PurposeThis study aims to develop a numerical identification and characterization of crack propagation through the use of a new optimization metaheuristics called Lichtenberg optimization.Design/methodology/approachThe damage-identification problem is treated as an inverse problem, which combines finite element methods with intelligent computational methods to obtain the best possible response. To optimize the objectives, the Lichtenberg algorithm is applied, which includes concepts of random cluster growth in nature.FindingsThe simulations show that it is possible to determine the Lichtenberg spectrum algorithm a part of the structure to be removed and replaced in this case to stop the propagation.Originality/valueThe results show a very good crack identification in plates-like structures using the Lichtenberg algorithm (LA) based only in strain fields. Although many studies have reported on damage-identification-based optimization methods, very few have focused on the crack tip modeling and LA as the main solver.

Supervisión de salud estructural mediante algoritmos de optimización Fireflyy elementos finitos

In this study, a novel technique for multiple damage detection of structures using modal characterization to evaluate the dynamic response of the structure given a damage model is investigated. The damage identification problem is seen as an optimization problem to be solved using a firefly optimization algorithm. The objective function is based on a numerical damage model that considers the modal response of the structures. We show some implementation details and discuss the obtained results for a benchmark problem used to assess the performance of the method and its advantages for structural health monitoring

Inverse structural damage identification problem in CFRP laminated plates using SFO algorithm based on strain fields

Damage detection methods are an important field of engineering and crucial in terms of structural safety. However, in many practical cases, the process of monitoring and identifying damage is extremely difficult or even impractical due to the conditions of access and operation of a given component/structure. In this study, an inverse algorithm based on strain fields for damage identification in composite plate structures is presented. The inverse analyses combine experimental tests and digital image correlation (DIC) with numerical models based on finite element update method with great advantage of being a non-contact method. The proposed technique identifies the location and dimension of damages in a CFRP plate using static strains formulated as an objective function to be minimized. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. A powerful new metaheuristic sunflower optimization (SFO) is employed to update the unknown model parameters. Experimental results showed the excellent efficiency in the combined use of DIC, numerical modeling and SFO optimization to accurately identify the location of damage in numerical and experimental tests. The obtained results indicate that the proposed method can be used to determine efficiently the location and dimension of structural damages in mechanical structures.

Damage identification of structures using second-order approximation of Neumann series expansion

In this paper, a new method proposed for structural damage detection from limited number of sensors using extreme learning machine (ELM). One of the main challenges in structural damage identification problems is the limitation in the number of used sensors. To address this issue, an effective model reduction method has been proposed. To condense mass and stiffness matrices, the second-order approximation of Neumann series expansion (NSEMR-II) has been used. Mode shapes and frequencies of damaged structures and corresponding generated damage states used as input and output to train extreme learning machine, respectively. To show the effectiveness of presented method, three different examples consists of a truss structure, irregular frame and shear frame have been studied. The obtained results show the ability of the proposed approach in identifying and estimating different damage cases using limited numbers of installed sensors and noisy modal data.

Structural damage identification of truss structures using self-controlled multi-stage particle swarm optimization

The present work proposes a self-controlled multi-stage optimization method for damage identification of structures utilizing standard particle swarm optimization (PSO) algorithm. Damage identification problem is formulated as an inverse optimization problem where damage severity in each element of the structure is considered as optimization variables. An efficient objective function is formed using the first few frequencies and mode shapes of the structure. This objective function is minimized by a self-controlled multi-stage strategy to identify and quantify the damage extent of the structural members. In the first stage, standard PSO is utilized to get an initial solution to the problem. Subsequently, the algorithm identifies the most damage-prone elements of the structure using an adaptable threshold value of damage severity. These identified elements are included in the search space of the standard PSO at the next stage. Thus, the algorithm reduces the dimension of the search space and subsequently increases the accuracy of damage prediction with a considerable reduction in computational cost. The efficiency of the proposed method is investigated and compared with available results through three numerical examples considering both with and without noise. The obtained results demonstrate the accuracy of the present method can accurately estimate the location and severity of multi-damage cases in the structural systems with less computational cost.

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.

Novel TQWT Algorithm for Structural Damage Identification

In this paper tunable Q-factor wavelet transform is implemented into damage identification. Fixed – Fixed beam damage identification problem is demonstrated. Translation and Rotational mode shapes are used as an input signal, the TQWT algorithm depends Q-factor and asymptotic redundancy, when it matches with the oscillatory behavior of the input signal it is tuned. This method decomposes a signal into a high-Q-factor and low-Q-factor component, and it can be used to differentiate the damaged and undamaged mode shapes of the beam structure. TQWT coefficient is used as damage index to locate and quantify the damage. Proposed method evaluated experimentally and results shows TQWT algorithm has a potential to detect even a small damage (10% stiffness loss) present in the structure.

Damage identification for frame structures using vision-based measurement

Extracting physical parameters for damage identification problems from full-field measurements is a promising research because of the recent spread of vision-based measurement techniques in the experimental mechanics. This paper presents a vision-based measurement framework using the camera system for damage identification. The framework is composed of four procedures: camera calibration, image processing, system identification and sensitivity analysis. In contrast to traditional finite-point measurements, the camera system allows considerably greater non-contact measurement flexibility. Such flexibility has two important benefits: first, less number of modes is required for modal-based damage identification problems; and second, more physical parameters could be extracted, taking advantage of the plentiful experimental data. A laboratory test comparing the camera system to traditional accelerometer measurement is conducted to confirm the above advantages. Further statistic analysis shows that the major drawback of this technique is that the camera system presents high levels of noise in small vibration responses at higher frequencies. Suitable strategies to circumvent this disadvantage are developed. Moreover, a technique for practical camera calibration without the requirement that the objective plane should be strictly perpendicular to the camera axis is also demonstrated and verified by the proposed laboratory test.

Modeling errors due to Timoshenko approximation in damage identification

SummaryThe use of accurate computational models for damage identification problems may lead to prohibitive costs. Damage identification problems are often characterized as inverse ill‐posed problems. Thus, the use of approximate models such as simplified physical and/or reduced‐order models typically yields misleading results. In this paper, we carry out a preliminary study on a particular simplified physical model, the Timoshenko beam model in the context of damage identification. The actual beam is a two‐dimensional relatively high aspect ratio (thickness/length) beam with a distributed damage that is modeled as a spatially varying Young modulus. We state the problem in the Bayesian framework for inverse problems and carry out approximative marginalization over the related modeling errors. The numerical experiments suggest that the proposed approach yields more stable results than using the Timoshenko beam model as an accurate model. Due to the severity of the Timoshenko approximation, however, the posterior error estimates of the proposed approach are not always feasible in the probabilistic sense.

Data-driven method of damage detection using sparse sensors installation by SEREPa

This paper presents a model-based method of damage detection and severity identification in structural elements. The model-based method is performed with a finite element model. One of the important challenges in damage identification problems is lack of measured degrees of freedom and limitations of installed sensors on the structures. The new approach of this study is the use of expanded mode shapes data to train artificial neural network (ANN). In this study, the measured mode shapes are expanded by SEREPa. SEREPa expansion is developed based on the System-Equivalent Reduction and Expansion Process (SEREP), which is a non-smooth method and protects the measured data. ANN was then trained through the expanded data as inputs, location and severity of damage as outputs. The algorithm used to train ANN is scaled conjugate gradient. The advantage of this algorithm is that less data storage space is used and lower computation costs are needed. To show SEREPa’s efficiency in estimating unmeasured mode shapes, an experimental example containing a truss tower was presented. Two numerical examples including a plane truss and a space truss were presented to illustrate efficiency of damage detection method. Finally, the proposed method was verified by an experimental example. Damage prediction results for both numerical and experimental examples indicated an acceptable accuracy of the proposed method.

Simultaneous identification of structural stiffness and mass parameters based on Bare-bones Gaussian Tree Seeds Algorithm using time-domain data

This paper mainly illustrates the Tree Seeds Algorithm (TSA) to tackle structural damage identification problem. The damage model is simulated by the alterations of both stiffness and mass parameters. The objective function is introduced by minimizing the differences between the measured and calculated acceleration data. To enhance the performance of the standard TSA, two modifications including the bare-bones Gaussian updated mechanism and the withering process are introduced. The modified algorithm is named after the BGTSA. In the numerical simulation part, the BGTSA is firstly used to make comparisons with several state-of-the-art algorithms on the CEC05. Secondly, the BGTSA is utilized to deal with the structural damage identification problem by optimizing the acceleration-based nonlinear objective function. Numerical experiments involving a simply supported beam and a truss are carried out to verify the effectiveness of the proposed algorithm. The final results show that with low amount of acceleration data, the BGTSA can acquire better identification results compared with other evolutionary algorithms. Therefore the proposed algorithm could be viewed as a potential tool to solve the structural damage identification problem.

Structural damage assessment with incomplete and noisy modal data using model reduction technique and LAPO algorithm

The present article proposes an efficient method for damage assessment of structures with incomplete and noisy modal data using model reduction method and Lightning Attachment Procedure Optimisation (LAPO) algorithm. The damage identification problem is formulated as an optimisation problem, in which the extent of damaged elements and a combination of both natural frequencies and modal flexibility changes are considered as the continuous design variables and a hybrid objective function, respectively. The LAPO algorithm, a recently developed algorithm, is for the first time extended to solve the formulated optimisation problem. In addition, due to incomplete modal data, an iterated improved reduction system (IIRS) technique is adopted to condense structural physical properties. The efficiency and robustness of the proposed method are investigated using three types of structures and the results are compared with those obtained by other well-known optimisation algorithms. It is shown that regardless of the effect of incomplete and noisy modal data, the proposed method not only successfully determines the location and extent of single and multiple damage cases in the structures, but also has lower computational cost than the other optimisers.

Probabilistic damage identification incorporating approximate Bayesian computation with stochastic response surface

Abstract Compared with classic probability or statistical theories, Bayesian inference could be a better option for real-world damage identification problems since it can simultaneously utilize both prior knowledge and current measurements of a structure. However for a complex problem, a Bayesian identification procedure should face the challenge of unknown likelihood functions and unacceptable computational expenses. Due to it, approximate Bayesian computation (ABC) is adopted and incorporated with the Metropolis Hastings sampling (MHS) algorithm and stochastic response surface (SRS). Likelihood functions are no longer required during the inference. A stable Markov chain is obtained containing qualified parameter samples, whose random responses are then fast computed by SRSs. Additionally, a nested Bayesian updating procedure with a dynamic tolerance is proposed for fast estimation convergence of posterior probability distributions. After that a probabilistic damage index is defined based on the statistical features of the estimated posterior probability distributions. Lastly, the feasibility of the proposed method has been validated using both numerical and experimental beams. Their damage was indicated by the magnitude variations of the damage index.

A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making

ABSTRACTAn inverse problem of damage identification and localization in a structure is modelled as a robust optimization problem. In the robust optimization problem, the optimum value and small variations around this optimum value are considered. The structural health monitoring damage detection problem is solved using a multiobjective genetic algorithm. So, the robust optimum value is obtained by solving a multiobjective problem where a functional and a variance function of this functional are used. This variance function is obtained by a Design of Experiment with regression and also through a relation between functional variance and damage parameters found by artificial neural network. As a multiobjective genetic algorithm obtains multiple solutions, a fuzzy decision making technique finds the better tradeoff solution for the problem. Boundary element method is utilized to obtain the distribution of stress to elastostatic problem. Numerical results clearly show that the proposed strategy and the use an optimized fuzzy decision making results in accurate damage identification and represents a powerful tool for structural health monitoring. Based on the analysis and numerical results, suggestions to potential researchers have also been provided for future scopes.

Modular Bayesian damage detection for complex civil infrastructure

We address the problem of damage identification in complex civil infrastructure with an integrative modular Bayesian framework. The proposed approach uses multiple response Gaussian processes to build an informative yet computationally affordable probabilistic model, which detects damage through inverse updating. Performance of structural components associated with parameters of the developed model was quantified with a damage metric. Particular emphasis is given to environmental and operational effects, parametric uncertainty and model discrepancy. Additional difficulties due to usage of costly physics-based models and noisy observations are also taken into account. The framework has been used to identify a reduction of a simulated cantilever beam elastic modulus, and anomalous features in main/stay cables and bearings of the Tamar bridge. In the latter case study, displacements, natural frequencies, temperature and traffic monitored throughout one year were used to form a reference baseline, which was compared against a current state, based on one month worth of data. Results suggest that the proposed approach can identify damage with a small error margin, even under the presence of model discrepancy. However, if parameters are sensitive to environmental/operational effects, as observed for the Tamar bridge stay cables, false alarms might occur. Validation with monitored data is also highlighted and supports the feasibility of the proposed approach.

Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods

Delamination is one of the most common failure mode in laminated composites that leads the separation along the interfaces of the layers. The structural performance can be significantly affected by this degradation. Such damages are not always visible on the surface and could potentially lead to catastrophic structural failures. The existence of delamination alters the vibration characteristics of the laminated structures, so if they are detected and measured previously, they can be used as indicator for quantifying health and the potential risk of catastrophic failures. To ensure structural performance and integrity, accurate Structural Health Monitoring (SHM) is crucial. In this study, an optimized methodology for delamination identification on laminated composite plates involving the use of reduced mode shapes and computational tools, i.e., Genetic Algorithm (GA) and Artificial Neural Networks (ANN) is performed. In a first step, the sensor distribution on the surface of the structure was optimized using Fisher Information Matrix (FIM) criteria. After, GA and ANN were applied in order to identify and predict delamination location. A feed-forward based neural network is used in order to detect damage on the laminated plate using data obtained from Finite Element Analysis (FEA). The present methodology identifies damage localization in structures and also quantifies damage severity. The applicability of the technique is demonstrated on laminated plates and results are compared with numerical algorithms. This paper shows the effectiveness of GA and ANN as tools for delamination damage identification problem. The algorithms in their inverse formulations are capable of predicting accurately delamination position in plates-like structures.

The Study on Bridge Damage Identification Based on Vibration Mode Analysis and Hadoop Platform

The methods of damage identification of bridge structures are developing rapidly and various. With the development of science and technology, especially the development of cloud computing technology in recent years, which has brought great convenience to human life, at the same time, it may also provide a new technical way to solve the problem of bridge damage identification. As an open source and distributed system architecture, the Hadoop is the most extensive research and applied data processing platform at home and abroad. Hadoop platform processes the data collected by tests aisles objectively and properly, makes the bridge damage identification more accurate and credible.

A fast and robust method for damage detection of truss structures

• This paper proposes a two-stage damage detection method for truss structures. • First stage focused on the damage localization and in the second stage, the damage severities are evaluated. • To localize the suspected damaged elements, using the concept of residual force vector, a new index is proposed. • To evaluate the damage severities, the damage detection problem formulated as an optimization problem and solved by GA. Use of optimization search algorithms is recognized as an efficient method for solving structural damage identification problems. Although these algorithms demonstrated their robustness to identify the location and extent of multiple damages in structural systems, they impose so much computational efforts to the damage assessing process that it reduces their attraction. In this paper by utilizing the concept of residual force vector, an efficient approach based on a Truss Element Damage Index (TEDI) is defined to assist in a fast and reliable prediction of damaged elements. Based on the proposed technique, the first step focuses on location detection of most probable damaged members. The healthy members will then be eliminated from the total list of variables. This can reduce the computational effort significantly. In the second step to identify damaged locations and severities, the Genetic Algorithm is employed to search for the optimum solution in the new search space resulted from the first step. Three test examples are considered to investigate the efficiency of proposed method for damage identification.