Damage Detection Process Research Articles

Substructure Damage Detection Method for Shear Structure Using Sub-Time Series and ARMAX

A local damage detection method for shear structure based on substructure approach and sub-time series superposition is proposed, which is promising for application in parallel and distributed damage detection systems. This method requires only three sensors to identify localized damage in any story of a building and it doesnt require constructing the structure models. A substructure method was used in this method to divide a complete structure into several substructures. Each substructure has a considerably smaller number of degrees of freedom (DOFs) which makes fewer requirements on the computing power of the data processing system. The algorithm is simple, which involves only algebra calculation and does not need any iterative computation. Moreover the damage detection process can be independently conducted on each substructure. Thus, this method is easy, efficient and suitable for being embedded into parallel and distributed damage detection systems. To better assess the performance of the method proposed above, experimental verification of the proposed approach has been conducted, which shows the proposed method works quite well and stably.

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

A considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures such as beams or plates. In the application on a complex structure, such as steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates and then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple-damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

An efficient approach of modelling the flexural cracking behaviour of un-notched plain concrete prisms subject to monotonic and cyclic loading

In the field of vibration-based damage detection of concrete structures efficient damage models are needed to better understand changes in the vibration properties of cracked structures. These models should quantitatively replicate the damage mechanisms in concrete and easily be used as damage detection tools. In this paper, the flexural cracking behaviour of plain concrete prisms subject to monotonic and cyclic loading regimes under displacement control is tested experimentally and modelled numerically. Four-point bending tests on simply supported un-notched prisms are conducted, where the cracking process is monitored using a digital image correlation system. A numerical model, with a single crack at midspan, is presented where the cracked zone is modelled using the fictitious crack approach and parts outside that zone are treated in a linear-elastic manner. The model considers crack initiation, growth and closure by adopting cyclic constitutive laws. A multi-variate Newton–Raphson iterative solver is used to solve the non-linear equations to ensure equilibrium and compatibility at the interface of the cracked zone. The numerical results agree well with the experiments for both loading scenarios. The model shows good predictions of the degradation of stiffness with increasing load. It also approximates the crack-mouth-opening-displacement when compared with the experimental data of the digital image correlation system. The model is found to be computationally efficient as it runs full analysis for cyclic loading in less than 2min, and it can therefore be used within the damage detection process.

Probabilistic optimum inspection planning of steel bridges with multiple fatigue sensitive details

Inspection planning of fatigue sensitive bridges is a complex process. This, in part, is due to various uncertainties associated with the damage propagation and the detection technique used to perform inspections. The planning process starts with identifying the most critical locations of fatigue damage occurrence. In this context, non-destructive testing (NDT) techniques, applied at different times along the service life, can be used to monitor the crack growth and support the fatigue life estimation. The next step is to establish an inspection plan which considers the available budget and the target performance level of the investigated bridge. This paper proposes a probabilistic approach for providing effective inspection plans for fatigue critical bridges. The proposed approach considers multiple fatigue critical locations in the bridge and chooses the best inspection option among a set of predefined types of non-destructive inspections for each detail. Uncertainties in the damage propagation are considered in addition to the uncertainties in the damage detection process. A multi-objective optimization problem, which accounts for various critical details in the bridge, is formulated to find the optimum inspection times and the required NDT technique for each inspection. An existing fatigue critical steel bridge is used to illustrate the proposed probabilistic approach.

Image Processing for Automated Flaw Detection and CMYK model for Color Image Segmentation using Type 2 Fuzzy Sets

Infrared (IR) thermography has evolved in recent years from being an emerging nondestructive testing (NDT) technique to a viable approach for both aerospace manufacturing and in-service inspections. One of the drawbacks of thermography techniques is that no standard signal processing has been universally adopted and that different algorithms yield different sizing results. Additionally, the data interpretation is not as simple as with other NDT techniques. In this paper the most common signal processing techniques applied to pulsed thermography, which include derivative processing, pulsed phase thermography and principal component analysis, are applied in an attempt to simplify the damage detection and sizing process. The pulsed thermography experiments were carried out on 25 impacted panels made of carbon fiber epoxy material. Despite using similar panels and the same experiment parameters, the damage detection and sizing processes are not straight forward. It is concluded that some algorithms provide easier detection capability than others. However, on their own, the different algorithms lack the robustness to make the damage detection and sizing processes reliable and fully automated. And Optimization of the similarity measure is an essential theme in medical image registration. In this paper, a novel continuous medical image registration approach (CMIR) is proposed. This is our extension work of the previous one where we did a segmentation part of any particular image with a custom algorithm .The CMIR, considering the feedback from users and their preferences on the trade-off between global registration and local registration, extracts the concerned region by user interaction and continuously optimizing the registration result. Experiment results show that CMIR is robust, and more effective compared with the basic optimization algorithm. Image registration, as a precondition of image fusion, has been a critical technique in clinical diagnosis. It can be classified into global registration and local registration. Global registration is used most frequently, which could give a good approximation in most cases and do not need to determine many parameters. Local registration can give detailed information about the concerned regions, which is the critical region in the image. Finding the maximum of the similarity measure is an essential problem in medical image registration. Our work is concentrating on that particular section with the synergy of Tpe-2 fuzzy logic invoked in it.

Wavelet Denoising in Electrical Resistance Based Damage Detection of Carbon Fiber Composite Materials

As carbon fiber reinforced polymer composites (CFRPs) are multifunctional materials in which the damage is coupled with the change in material electrical resistance, the use of electrical conductivity monitoring can provide real-time information concerning the damage state through examining the change in resistance/electric potential. It has been shown that, in some recent work, resistance measurement allows the monitoring of the in-situ evolution of various internal damage nucleation and growth phenomena such as fiber fractures, interply matrix cracks and interply delamination. However, one of the common difficulties in measuring the small changes of electrical signals due to failures in the CFRPs is the contamination of extraneous noises during the detection process. In this paper, signal analysis procedures are applied to minimize or eliminate the contaminated noise during the damage detection process, as to maximize the accuracy of the damage detection process. In addition to the traditional time signal averaging technique, the Fast Fourier Transform (FFT) is applied to filter at the narrow band near the input frequency as to eliminate the unwanted noises. However, based on laboratory experiments, the authors have found that the frequency filtering techniques will retain significant inaccuracy when extraneous noises are in the neighborhood of the input frequency, and, in some cases, accurate measurement of the electrical signals can be susceptable when noise components are significantly higher than the original input signal. To overcome this drawback, the use of a joint time-frequency technique with wavelet transforms has been introduced. As a joint time-frequency signal analysis tool, the wavelet transforms offer simultaneous interpretation of the signal in both time and frequency domains which results in signal filtering effects in both time and frequency domains. Experimental results by the authors have shown significant improvement in accuracy when using the wavelets than those of the FFT and the time averaging techniques. This paper demonstrates the wavelet transform applications in electrical signal noise reduction of CFRPs as well as the use of both continuous wavelet (CWT) and discrete wavelet (DWT) transforms. It has been found that when noise is small, the DWT is more appropriate as the original signal can be reconstructed to offer better comparative information. The use of CWT will provide better results when random noises are significantly higher. Although the use of CWT does not have the ability revert back to the original signal, the comparison of the CWT coefficients can offer more valuable information concerning the damage occurrences while the repeated applications of DWT will result in accumulated errors such that the original shapes of the wavelet cannot be correctly reconstructed.

Information entropy based algorithm of sensor placement optimization for structural damage detection

The structural health monitoring (SHM) benchmark study on optimal sensor placement problem for the instrumented Canton Tower has been launched. It follows the success of the modal identification and model updating for the Canton Tower in the previous benchmark study, and focuses on the optimal placement of vibration sensors (accelerometers) in the interest of bettering the SHM system. In this paper, the sensor placement problem for the Canton Tower and the benchmark model for this study are first detailed. Then an information entropy based sensor placement method with the purpose of damage detection is proposed and applied to the benchmark problem. The procedure that will be implemented for structural damage detection using the data obtained from the optimal sensor placement strategy is introduced and the information on structural damage is specified. The information entropy based method is applied to measure the uncertainties throughout the damage detection process with the use of the obtained data. Accordingly, a multi-objective optimal problem in terms of sensor placement is formulated. The optimal solution is determined as the one that provides equally most informative data for all objectives, and thus the data obtained is most informative for structural damage detection. To validate the effectiveness of the optimally determined sensor placement, damage detection is performed on different damage scenarios of the benchmark model using the noise-free and noise-corrupted measured information, respectively. The results show that in comparison with the existing in-service sensor deployment on the structure, the optimally determined one is capable of further enhancing the capability of damage detection.

System identification method by using inverse solution of equations of motion in frequency domain

In this paper a direct system identification method is presented for identification of general linear system physical parameters (structural mass, damping and stiffness matrices) using direct inverse solution of the equations of motion in the frequency domain. When the input and output (responses) measured data are noise free, the proposed method can identify physical parameters exactly. When the input and outputs are mixed with measurement noise, an optimization process has been added to the method in order to improve the accuracy of the identified physical parameters of the system. The validity and efficiency of the proposed method is tested on a multistory-non-proportional-damping-structure subjected to sweep harmonic forces at the first story. It is shown that the proposed method can estimate the system parameters with a high level of accuracy. In the case of Gaussian white noise root mean square (RMS) is equal to 3% of the RMS of the structure response, stiffness matrix will be identified with around 2% error. Therefore identified matrices could be used for the damage detection process. The sensitivity of the proposed method to the noise level and damping of structure has also been investigated.

Performance Degradation Assessment of a Beam Structure by Using Wavelet Packet Energy

The evaluation on damage event of a beam structure is carried out in this study through an integration of knowledge in damage detection and signal process. A damage detection approach based on wavelet packet transform (WPT) is developed by decomposing and reconstructing the structural dynamic responses to obtain the signal energy distribution at different levels. The WPT coefficients are reconstructed to extract the signals in various frequency ranges and determine the signal energy in different frequency bands. A WPT component energy index is proposed to establish the slope vector as well as curvature vector and then the judge on the slope vector and curvature vector can indicate the damage event. Parametric study is carried out to investigate the relationship between the index amplitude and the damage extent. The results from this study demonstrate that the detection approach proposed can accurately identify the damage event in the structure.

An Improved Method to Detect Damage Using Modal Strain Energy Based Damage Index

This paper presents two novel concepts to enhance the accuracy of damage detection using the Modal Strain Energy based Damage Index (MSEDI) with the presence of noise in the mode shape data. Firstly, the paper presents a sequential curve fitting technique that reduces the effect of noise on the calculation process of the MSEDI, more effectively than the two commonly used curve fitting techniques; namely, polynomial and Fourier's series. Secondly, a probability based Generalized Damage Localization Index (GDLI) is proposed as a viable improvement to the damage detection process. The study uses a validated ABAQUS finite-element model of a reinforced concrete beam to obtain mode shape data in the undamaged and damaged states. Noise is simulated by adding three levels of random noise (1%, 3%, and 5%) to the mode shape data. Results show that damage detection is enhanced with increased number of modes and samples used with the GDLI.

A probabilistic damage detection approach using vibration-based nondestructive testing

With the aim of improving the accuracy of the assessment of existing structures, damage detection using vibration-based nondestructive testing (NDT) has been extensively studied. It has been recognized that a considerable amount of uncertainties exist in the damage detection process. This paper proposes a novel probabilistic damage detection approach that accounts for the underlying uncertainties. The proposed approach combines two techniques: A Bayesian model updating and a vibration-based damage identification technique (VBDIT). The model updating uses modal frequencies from a damaged structure to build a baseline finite element model (FEM). VBDIT uses mode shapes from the baseline model and the damaged structure to detect damage at local level. The proposed framework makes use of the advantages of the Bayesian model updating and the VBDIT, and compensates for their drawbacks. The sources and types of errors that may occur in the damage detection process are discussed and considered in the proposed formulation. In particular, the proposed approach considers the measurement errors in the vibration tests, the modeling errors in the damage detection process, and the statistical uncertainties in the unknown model parameters. As an application, a finite element model simulating a two-span aluminum beam is used to illustrate the proposed framework. The effects of the measurement and modeling errors on the performance of the proposed damage detection are studied. Modal data can be easily extracted from out-put only responses on an existing structure, making the proposed methodology of practical value.

Nonlinear features for damage detection on large civil structures due to earthquakes

This article proposes the degree of nonlinearity as a feature for damage detection on large civil structures due to earthquakes. The degree of nonlinearity, which is a numerical value, represents how nonlinear structures behave during earthquakes. The degree of nonlinearity is computed directly from the data of the ground motion and vibration of the structure. The computation of the degree of nonlinearity uses a Hilbert transform and does not require any other nonlinear structural or mathematical models. Therefore, the damage detection process is very fast. Thus, an immediate decision about the condition of structures after strong earthquakes is possible. This article shows theoretically that a plot between the degree of nonlinearity against the magnitude of the ground motion represents the behavior of a structure. This plot is considered the signature of that structure. A structure that is healthy will have its specific healthy signature. If a new earthquake strikes and the new degree of nonlinearity deviates from its healthy signature, the structure may be damaged by that earthquake. To verify the validity of the degree of nonlinearity, numerical studies of nonlinear systems are presented. Although the application of degree of nonlinearity on a real civil structure is still not totally conclusive due to the limitation of data, the results from currently available data are encouraging. With more investigation, the degree of nonlinearity could be an additional or alternative feature for vibration-based structural health monitoring.

Performance evaluation of modified genetic and swarm-based optimization algorithms in damage identification problem

An experimental verification of a damage detection process using novel optimization techniques such as modified real coded genetic algorithms and swarm-based algorithms is presented. Here, the objective function is defined as the sum of differences of the modal frequencies between intact and stiffness damaged state, which has to be minimized to identify the damage location and its severity in the process of model updating. In addition to the structural or damage variables such as the mass or stiffness of the numerical model, the profiles of modal frequency shifts are also damage-sensitive features. The iterative process that uses the proposed population-based optimization algorithms successfully identifies the local mass change of a test structure by updating the damage variables to fit the modal data of test structures such as a cantilevered beam and multibay truss frame. Copyright © 2011 John Wiley & Sons, Ltd.

A wavelet-based damage diagnosis algorithm using principal component analysis

SUMMARY The applicability of the Haar and Morlet wavelet transforms of the vibration signals for structural damage diagnosis was demonstrated in previous papers by the authors. Two significant developments that followed the initial research are presented in the current paper. In the first part of the paper, a data preprocessing algorithm is developed to enable optimal signal selection from a database of baseline signals for damage diagnosis purposes. The second part of the paper describes the extraction of the damage sensitive feature vector as a function of the energies at the fifth, sixth, and seventh dyadic scales of the vibration signal that serve for damage detection. Both data preprocessing and feature extraction models involve the use of principal components analysis. The process of damage detection is automated using the k-means algorithm and the gap statistic. A simple damage extent measure is also discussed. Finally, the migration of damage sensitive feature vectors with increased damage is illustrated for vibration signals obtained from the American Society of Civil Engineers' benchmark structure numerical simulations. The results indicate that the proposed algorithm is able to consistently detect and quantify damage for the damage patterns specified by the American Society of Civil Engineers' benchmark experiment. Copyright © 2011 John Wiley & Sons, Ltd.

A substructure approach to local damage detection of shear structure

SUMMARY We propose a substructure approach that allows for the local damage detection of a shear structure. This method requires only three sensors to identify localized damage in any story of a shear structure building. A substructure approach was used in this method to divide a complete structure into several substructures in order to significantly reduce the number of unknown parameters for each substructure so that damage detection processes can be independently conducted on each substructure. Thus, our method is suitable for use in a parallel and distributed damage detection system. Simulations and experiments on five-story buildings were conducted to test the feasibility of our proposed method. Copyright © 2011 John Wiley & Sons, Ltd.

Influence of the Autoregressive Model Order on Damage Detection

Abstract: An important step for using time-series autoregressive (AR) models for structural health monitoring is the estimation of the appropriate model order. To obtain an optimal AR model order for such processes, this article presents and discusses four techniques based on Akaike information criterion, partial autocorrelation function, root mean squared error, and singular value decomposition. A unique contribution of this work is to provide a comparative study with three different AR models that is carried out to understand the influence of the model order on the damage detection process in the presence of simulated operational and environmental variability. A three-story base-excited frame structure was used as a test bed in a laboratory setting, and data sets were measured for several structural state conditions. Damage was introduced by a bumper mechanism that induces a repetitive impact-type nonlinearity. The operational and environmental effects were simulated by adding mass and by changing the stiffness properties of the columns. It was found that these four techniques do not converge to a unique solution, rather all require somewhat qualitative interpretation to define the optimal model order. The comparative study carried out on these data sets shows that the AR model order range defined by the four techniques provides robust damage detection in the presence of simulated operational and environmental variability.

A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data

Damage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the damage features and how they propagate during the damage detection process also contribute to uncertainties in SHM. This paper introduces an integrated method for damage feature extraction and damage recognition. We describe a robust damage detection method that is based on using artificial neural network (ANN) to compute the wavelet energy of acceleration signals acquired from the structure. We suggest using the wavelet energy as a damage feature to classify damage states in structures. A case study is presented that shows the ability of the proposed method to detect and pattern damage using the American Society of Civil Engineers (ASCEs) benchmark structure. It is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy to varying levels of damage.

A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing

This paper introduces a novel approach for optimal sensor and/or actuator placement for structural health monitoring (SHM) applications. Starting from a general formulation of Bayes risk, we derive a global optimality criterion within a detection theory framework. The optimal configuration is then established as the one that minimizes the expected total presence of either type I or type II error during the damage detection process. While the approach is suitable for many sensing/actuation SHM processes, we focus on the example of active sensing using guided ultrasonic waves by implementing an appropriate statistical model of the wave propagation and feature extraction process. This example implements both pulse-echo and pitch-catch actuation schemes and takes into account line-of-site visibility and non-uniform damage probabilities over the monitored structure. The optimization space is searched using a genetic algorithm with a time-varying mutation rate. We provide three actuator/sensor placement test problems and discuss the optimal solutions generated by the algorithm.

Statistical detection of structural damage based on model reduction

This paper proposes a statistical method for damage detection based on the finite element (FE) model reduction technique that utilizes measured modal data with a limited number of sensors. A deterministic damage detection process is formulated based on the model reduction technique. The probabilistic process is integrated into the deterministic damage detection process using a perturbation technique, resulting in a statistical structural damage detection method. This is achieved by deriving the first- and second-order partial derivatives of uncertain parameters, such as elasticity of the damaged member, with respect to the measurement noise, which allows expectation and covariance matrix of the uncertain parameters to be calculated. Besides the theoretical development, this paper reports numerical verification of the proposed method using a portal frame example and Monte Carlo simulation.

Condition Assessment of Structures Under Ambient White Noise Excitation

This paper proposes a damage detection method based on the wavelet packet energy of covariance of measured acceleration responses of structures under ambient excitation. It is a model-based method comparing the measured acceleration responses before and after damage occurrence. The ambient excitation is assumed to be white noise. A damage index based on the change of modal strain energy is used for damage localization. The damage quantification is performed using the sensitivity of the wavelet packet energy of the covariance function. A cantilever five-bay truss structure is used to demonstrate the efficiency of the method with three damage scenarios. The combined two-stage approach is shown capable of locating and accurately quantifying local damages in the presence of measurement noise. A nine-bay three-dimensional truss structure is also assessed in the laboratory with the proposed method, and the whole process of damage detection with incomplete and noisy measurement, initial model error, etc., is demonstrated to be successful for identifying, with good accuracy, local damages in members.