Damage Identification Problem Research Articles

Identificação de Danos Estruturais utilizando Dados no Domínio do Tempo e Critério D-ótimo

The present work deals with the damage identification problem in mechanical structures from their impulse response. In the adopted model, the structural integrity is continually described by a cohesion parameter and the finite element model (FEM) is used to spatially discretize the displacement and cohesion fields. The damage identification problem is then posed as an optimization one, whose objective is to minimize, with respect to the vector of nodal cohesion parameters, a functional based on the difference between the experimentally obtained impulse response and the corresponding one predicted by an FEM of the structure. The damage identification problem built on the time domain presents some advantages, as the applicability in linear systems with high levels of damping an/or closed spaced modes, and in nonlinear systems. Numerical studies were carried out considering a simply supported Euler-Bernoulli beam. The D-optimal criterion was considered with the aim at determining the optimal position of the displacement sensor. The Differential Evolution (DE) optimization method was considered to solve the inverse problem of damage identification. Numerical analysis were carried out in order to assess the influence, on the identification results, of noise in the synthetic experimental data and of the sensor position. The presented results shown the potentiality of the proposed damage identification approach and also the importance of the optimal experiment design for the quality of the damage identification results.

Multi-swarm fruit fly optimization algorithm for structural damage identification

In this paper, the Multi-Swarm Fruit Fly Optimization Algorithm (MFOA) is presented for structural damage identification using the first several natural frequencies and mode shapes. We assume damage only leads to the decrease of element stiffness. The differences on natural frequencies and mode shapes of damaged and intact state of a structure are used to establish the objective function, which transforms a damage identification problem into an optimization problem. The effectiveness and accuracy of MFOA are demonstrated by three different structures. Numerical results show that the MFOA has a better capacity for structural damage identification than the original Fruit Fly Optimization Algorithm (FOA) does.

Damage diagnosis in steel structures with different noise levels via optimization algorithms

In this paper, the main objective is to solve the inverse problem of damage identification with the help of the Imperialist Competitive Algorithm (ICA) optimization. Three different numerical cases, including a clamped-free beam, a 2D truss and a 2D plate-type structure are modelled by using Finite Element Method (FEM) and used to evaluate the proposed damage identification procedure. The proposed objective function for the optimization procedure is formed by using modal parameters. Those parameters are obtained from the damage state, where cracks are simulated with the assumption of reducing the local stiffness of the structure. Then, proposed damage detection/characterization process is performed by implementing an optimization algorithm, called Imperialist Competitive Algorithm (ICA). Results obtained from the numerical case studies show that this algorithm is trustworthy and can be used to identify the severity and location of the damage with a good accuracy. Furthermore, the effects of noise on the results of damage identification process are studied so as to investigate the tolerance of the method in the face of environmental noise. Finally, the results obtained by the ICA are compared to the ones obtained by using two commonly used algorithms, i.e. the binary genetic algorithm (BGA) and particle swarm optimization (PSO).

Identificação de danos estruturais em placas baseada em um modelo de dano contínuo

The present work addresses the structural damage identification problem in plates by using the flexibility matrix. The damage is continuously described by a cohesion parameter defined in the elastic structure domain. The damage identification problem is, then, defined as an optimization one, whose aim is to minimize, with respect to the cohesion field, a functional based on the difference between the experimental flexibility matrix and the corresponding one predicted by a finite element model of the structure. The particle swarm optimization method was considered to solve the optimization problem. The numerical tests of the proposed damage identification method are carried out taking into account a rectangular Kirchhoff plate with one clamped edge. The considered damage scenario is composed by two damaged regions and the mode shapes are corrupted with different additive noise levels. The numerical results show the potentiality of the proposed method in identifying damages from a reduced number of noise corrupted experimental data.

The Differential Evolution method applied to continuum damage identification via flexibility matrix

In the present work, a structural damage identification approach, built on the flexibility matrix, is proposed. Here, the damage state of the structure is continuously described by a cohesion parameter, which, in turn, is spatially discretized by the finite element method. Then, the inverse problem of damage identification is defined as an optimization one, where the objective is to minimize, with respect to the nodal cohesion parameters, a functional based on the difference between the experimentally obtained flexibility matrix and the corresponding one predicted by a finite element model of the structure. The Differential Evolution stochastic optimization method was considered for solving the resulting damage identification problem. The assessment of the proposed approach has been performed by means of numerical simulations on a simply supported Euler–Bernoulli beam. A brief analysis of the influence of different damage positions and severities on the undamped natural frequencies and on the flexibility matrix of the structure is presented. Then, the influence of damage and different levels of noise on the mode shapes of the structure is also considered. In the damage identification problem, different damage scenarios and noise levels were addressed. For comparison purposes, other stochastic optimization methods, namely, Particle Swarm Optimization, Luus–Jaakola and Simulated Annealing, were also considered for the identification of one damage scenario in the presence of noise corrupted data.

Application of generalized flexibility matrix in damage identification using Imperialist Competitive Algorithm

In this article, a new objective function which is formed by using generalized flexibility matrix is presented with the aim of solving the constrained optimization problem in damage detection procedure. The main purpose is to decrease the effects of truncation error which ensue from cutting off the higher order modes. Two different case studies are used to evaluate the proposed approach. These cases include, a 2D-frame and a Howe-Truss structure. In this study, each structure is modeled by finite element method and damages are induced by considering a reduction in the stiffness of the elements. Finally, the inverse problem of damage identification is solved by using the Imperialist Competitive Algorithm (ICA) for each damage scenario. Not only the effect of noise on the obtained results is studied but also a comparison is drawn between the proposed method based on ICA and other conventional optimization algorithms, namely PSO and GA.

Damage diagnostic technique combining POD with time-frequency analysis and dynamic quantum PSO

A novel damage identification procedure is presented that is ideally suited for continuous structural health monitoring of large civil engineering structures. The procedure for damage detection, location and quantification is developed by combining Hilbert Huang transformation (HHT) and proper orthogonal decomposition with dynamic quantum particle swarm optimisation algorithm (DQPSO). The damaged structure is modeled using finite elements, in which the damage is represented by a section having reduced flexural rigidity. Forward analyses are first used to identify time instant of damage using HHT and location of damage using proper orthogonal decomposition. The inverse damage identification problem is later set up as an optimization problem and solved using a newly developed DQPSO algorithm. The proposed method exhibited robustness in identifying the exact time instant of damage occurrence, spatial locations of the damage and also the extent of damage correctly for a wide range of locations and damage severities. The effect of measurement error/noise is also investigated.

Variations of Ant Colony Optimization for the Solution of the Structural Damage Identification Problem

In this work the inverse problem of identification of structural stiffness coefficients of a damped spring-mass system is tackled. The problem is solved by using different versions of Ant Colony Optimization (ACO) metaheuristic solely or coupled with the Hooke-Jeeves (HJ) local search algorithm. The evaluated versions of ACO are based on a discretization procedure to deal with the continuous domain design variables together with different pheromone evaporation and deposit strategies and also on the frequency of calling the local search algorithm. The damage estimation is evaluated using noiseless and noisy synthetic experimental data assuming a damage configuration throughout the structure. The reported results show the hybrid method as the best choice when both rank-based pheromone deposit and a new heuristic information based on the search history are used.

Application of the Elastic Waves and Neural Networks as a Tool of Damage Detection and Health Monitoring in Aircraft's Structures

There exist a large group of structures like e.g. aircraft, which operational safety requires periodical inspections or even continuous monitoring of its health. Sometimes the structures are equipped with Structural Health Monitoring systems which usually utilizes the phenomena of elastic waves propagation in solids. The analysis of elastic waves signals consists in general of quantitative and qualitative description of theirs changes (e.g. attenuation, distortion, reflection) caused by a damage appearance and growth. Since the reflections and dispersion effects may produce pretty complex signals, therefore the determination of parameters suitable for damage detection requires the application of advance signal processing techniques. For this purpose an approach of novelty detection and damage evaluation based on soft computing methods (for example - Neural Networks) was proposed in this paper. Two levels of the damage identification problem were realized: novelty detection and damage assessment. The system accuracy and reliability were verified during laboratory tests. It was proved that the system can be used for the analysis of simple as well as complex signals. One of the important factors in the structural health monitoring systems is the amount of data that need to be analysed in real time. This study investigated the use of artificially deteriorated signals of elastic waves in training the novelty detection (ND) system. In this system auto-associative neural networks were trained using the principal components calculated on the basis of experimentally measured signals. It was found that the designed ND system remained sensitive and robust even when it uses raw signals with a relatively low sampling rate, on a fairly narrow time window and even noisy signals.

Three-step damage identification method based on dynamic characteristics

A three-step damage identification method based on dynamic characteristics is proposed to improve the structure reliability and security and avoid serious accident. In the proposed method, the frequency and difference of modal curvature (DMC) are used as damage indexes. Firstly, the detection of the occurrence of damage is addressed by the frequency or the square of frequency change. Then the damage location inside the structure is measured by the DMC. Finally, with the stiffness reduction rate as a damage factor, the amount of damage is estimated by the optimization algorithm. The three-step damage identification method has been validated by conducting the simulation on a cantilever beam and the shaking table test on a submerged bridge. The results show that the method proposed in this paper can effectively solve the damage identification problem in theory and engineering practice.

Studies on Damage Distinguishing of Underground Tunnel Structure Based on BP Neural Network

With the expansion and development of scale of construction on metro engineering, the damage diagnosis and the safety evaluation on underground engineering structure have become vital problems to be solved. This paper raised an idea to distinguish underground engineering structure based on BP neural network: define change rate of curvature of structure, and recognize it as the input scalar of BP neural network, using a reducing unit elastic modulus method to simulate damage location and damage degree, through various set of underground structure extent of damage, recognize the first four order curvature structure change rate as input of BP neural network. The results show that the method using BP neural network can identify the damage degree of underground engineering structure accurately and can solve the damage identification problem of underground engineering structure conveniently and effectively.

Acoustic Emission Source Identification Based on Pattern Recognition Method

A new pattern recognition method based on harmonic wavelet packet (HWPT) and hierarchy support vector machine (H-SVM) is proposed to solve the fatigue damage identification problem of helicopter component. In this approach, HWPT is used to extract the energy feature of acoustic emission (AE) signals on different frequency bands and to reduce the dimensionality of original data features. The H-SVM classifier is used to identify the AE source type. A subset of the experimental data for known AE source type is used to train the H-SVM classifier, the remaining set of data is used to test the H-SVM classifier. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The results indicate that the proposed approach can implement AE source type identification effectively, and has better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.3587

Probabilistic damage identification of structures with uncertainty based on dynamic responses

The probabilistic damage identification problem with uncertainty in the FE model parameters, external-excitations and measured acceleration responses is studied. The uncertainty in the system is concerned with normally distributed random variables with zero mean value and given covariance. Based on the theoretical model and the measured acceleration responses, the probabilistic structural models in undamaged and damaged states are obtained by two-stage model updating, and then the Probabilities of Damage Existence (PDE) of each element are calculated as the damage criterion. The influences of the location of sensors on the damage identification results are also discussed, where one of the optimal sensor placement techniques, the effective independence method, is used to choose the nodes for measurement. The damage identification results by different numbers of measured nodes and different damage criterions are compared in the numerical example.

Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.

An Analysis of the Effectiveness of Application of Rotation Rate Sensors in Non Destructive Damage Evaluation

This paper reports the analysis of the effectiveness of the application of rotation rate sensors in structural damage identification problem using harmonic vibrations for plexiglass models of cantilever beams. The two types of sensors are applied: angular rate sensors and conventional translational, piezoelectric accelerometers. The amplitudes of dynamic response under harmonic kinematic excitations were subsequently used in the stiffness reconstruction procedure. Particularly promising results are obtained by using only rotation rate sensors in the reconstruction procedure.

Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem

There is a wide variety of existing Genetic Algorithms (GA) operators and parameters in the literature. However, there is no unique technique that shows the best performance for different classes of optimization problems. Hence, the evaluation of these operators and parameters, which influence the effectiveness of the search process, must be carried out on a problem basis. This paper presents a comparison for the influence of GA operators and parameters on the performance of the damage identification problem using the finite element model updating method (FEMU). The damage is defined as reduction in bending rigidity of the finite elements of a reinforced concrete beam. A certain damage scenario is adopted and identified using different GA operators by minimizing the differences between experimental and analytical modal parameters. In this study, different selection, crossover and mutation operators are compared with each other based on the reliability, accuracy and efficiency criteria. The exploration and exploitation capabilities of different operators are evaluated. Also a comparison is carried out for the parallel and sequential GAs with different population sizes and the effect of the multiple use of some crossover operators is investigated. The results show that the roulettewheel selection technique together with real valued encoding gives the best results. It is also apparent that the Non-uniform Mutation as well as Parent Centric Normal Crossover can be confidently used in the damage identification problem. Nevertheless the parallel GAs increases both computation speed and the efficiency of the method.

Damage Identification Scheme Based on Compressive Sensing

Civil infrastructures are critical to every nation, due to their substantial investment, long service period, and enormous negative impacts after failure. However, they inevitably deteriorate during their service lives. Therefore, methods capable of assessing conditions and identifying damage in a structure timely and accurately have drawn increasing attention. Recently, compressive sensing (CS), a significant breakthrough in signal processing, has been proposed to capture and represent compressible signals at a rate significantly below the traditional Nyquist rate. Due to its sound theoretical background and notable influence, this methodology has been successfully applied in many research areas. In order to explore its application in structural damage identification, a new CS-based damage identification scheme is proposed in this paper, by regarding damage identification problems as pattern classification problems. The time domain structural responses are transferred to the frequency domain as sparse representation, and then the numerical simulated data under various damage scenarios will be used to train a feature matrix as input information. This matrix can be used for damage identification through an optimization process. This will be one of the first few applications of this advanced technique to structural engineering areas. In order to demonstrate its effectiveness, numerical simulation results on a complex pipe soil interaction model are used to train the parameters and then to identify the simulated pipe degradation damage and free-spanning damage. To further demonstrate the method, vibration tests of a steel pipe laid on the ground are carried out. The measured acceleration time histories are used for damage identification. Both numerical and experimental verification results confirm that the proposed damage identification scheme will be a promising tool for structural health monitoring.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses, a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification (SMI) and two-step model updating procedure. Due to the insufficiency and uncertainty of information obtained from measurements, the uncertain problem of damage identification is addressed with interval variables in this paper. Based on the first-order Taylor series expansion, the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated, respectively. The possibility of damage existence (PoDE) in elements is proposed as the quantitative measure of structural damage probability, which is more reasonable in the condition of insufficient measurement data. In comparison with the identification method based on a single kind of information, the SMI method will improve the accuracy in damage identification, which reflects the information fusion concept based on the non-probabilistic set. A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Crack identification in composite elements with non-linear geometry using spatial wavelet transform

A great popularity of constructional polymer composites in industrial applications requires the development of appropriate diagnostic methods for the elements made of these materials. These methods should provide damage detection and identification in possible early stages of its development. A large group of such methods is based on vibration testing and modal analysis. One of the promising techniques of processing vibration data for damage identification is a wavelet transform. Considering the practical needs the method should be applicable for plane structures with non-linear geometry. Results presented in this paper consider numerous analyses including the application of different types of wavelet transforms, different types of wavelet functions and other factors, which have an influence on the accuracy of damage identification. The proposed method shows good effectiveness in damage identification problems and could be applied in industrial conditions as well.

Soft computing methods in the analysis of elastic wave signals and damage identification

An outline of a Structure Health Monitoring (SHM) system is given. A non-destructive technique of elastic wave propagation was used in the presented approach. For the analysis of time signals recorded by piezoelectric transducers, signal processing algorithm has been developed and artificial neural networks have been used. As a consequence, two levels of the damage identification problem have been realized: novelty detection and damage assessment. The systems accuracy and reliability have been verified during laboratory tests of strip specimens made of various materials. It has been proved that the system can be used for the analysis of simple as well as complex signals. Moreover, the system can operate online as an automatic SHM system.