1,721,008 research outputs found
Structural health monitoring by a new hybrid feature extraction and dynamic time warping methods under ambient vibration and non-stationary signals
No full textFeature extraction and classification are crucial steps of a data-driven structural health monitoring strategy. One of the major issues in feature extraction is to extract damage-sensitive features from non-stationary signals under unknown ambient vibration. Furthermore, the use of high-dimensional features in damage detection is the other challenging issue, which may make a difficult and time-consuming process. This article is initially intended to propose a hybrid algorithm as a combination of EEMD technique and ARARX model for feature extraction. Subsequently, correlation-based dynamic time warping method is proposed to detect damage by using randomly high-dimensional multivariate features. Due to the importance of damage localization, dynamic time warping is eventually applied to locate damage. Experimental datasets of the IASC-ASCE benchmark structure are utilized to validate the accuracy of proposed methods. Results suggest that the proposed methods are effective tools for damage detection and localization under ambient vibration and non-stationary and/or stationary signals
Application of supervised learning to validation of damage detection
No full textUnsupervised learning methods are effective and suitable tools for damage detection. The main reason for the popularity of these methods in structural health monitoring originates from the fact that the process of learning can be implemented by information of the only normal condition called training data. In contrast, supervised learning methods require information of both normal and current conditions for the process of interest. Because civil engineering structures are expensive and complex, it is not reasonable and economical to impose intentional damage on providing training data. Hence, it is not simple to directly exploit supervised learning techniques in structural health monitoring. To deal with this limitation, this article proposes a novel two-level strategy including three algorithms for using the concepts of both unsupervised learning and supervised learning. The major contribution of this strategy is to consider supervised learning as a validation tool for damage detection. First, the results of damage detection are obtained from two unsupervised learning methods developed by Mahalanobis squared distance and a deep autoencoder neural network in the first two algorithms of the proposed strategy. The main objective is to separate accurate and confusing results of damage detection based on Type I and Type II errors. Second, the confusing results are fed into the third algorithm to train a classifier and compute their classification margins for making the final decision and validating damage detection. The effectiveness and applicability of the proposed strategy are assessed by a numerical concrete beam and an experimental laboratory frame. Results show that this strategy with the aid of the Naïve Bayes classifier enables the unsupervised learning methods to make accurate decisions
New sensitivity-based methods for structural damage diagnosis by least square minimal residual techniques
No full textThis paper presents new sensitivity-based methods for detection of structural damage using incomplete noisy modal data. These methods are based on the first-order derivative of modal parameters. Changes of natural frequency do not usually provide spatial information on the structural damage. They are also not sensitive to the local damage. In this paper, a new sensitivity function is proposed using method of Lagrange multipliers in order to deal with these weaknesses when applying natural frequency in the sensitivity-based damage diagnosis. Mode shape is the other vibrational data which leads to better results in comparison with natural frequency. However, usually some mode shape's sensitivities require all modes to obtain exact sensitivity functions. Thus, an improved sensitivity of mode shape is presented to constitute an applicable formulation based on using incomplete modes. To determine the damage quantity, a powerful iterative method named Least-Square Minimal Residual (LSMR) technique is proposed in the condition of incomplete modes. Subsequently, Regularized Least-Square Minimal Residual (RLSMR) method is presented to detect structural damage when the incomplete modal parameters are contaminated by noise. Applicability and effectiveness of the proposed methods are numerically verified using two practical examples consisting of a six-story shear building and a planner truss. Eventually, numerical results indicate that the LSMR and RLSMR are influential algorithms for precisely determining the damage severity. Furthermore, obtained results of damage diagnosis process in the free-noise data show that the proposed sensitivities of natural frequency and mode shape can provide reliable and accurate results for structural damage diagnosis
Damage detection in structural systems by improved sensitivity of modal strain energy and Tikhonov regularization method
Get PDFIn this article, new methods for detecting damage in structural systems are presented. These methods are categorized as damage localization and damage quantification, respectively. Hence, direct changes of modal strain energy are applied to identify locations of damage. Moreover, some restraints such as incomplete measured modes and simple assumptions in structural modeling may cause failure in the results of damage localization. Therefore, a correlation-based method is utilized to obviate these limitations and precisely detect damage sites. Subsequently, an improved sensitivity of modal strain energy is generated to determine damage severities. To achieve appropriate results in damage quantification, Tikhonov regularization approach is utilized instead of classical methods such as applying penalty function and current inverse problem techniques. Applicability and effectiveness of proposed methods are numerically verified using two practical examples consisting of a planner truss and a portal frame, respectively. Eventually, numerical results indicate that the proposed damage localization approach provides an influential algorithm for precisely identifying damage sites. Furthermore, obtained damage severities show that utilizing the sensitivity of modal strain energy and also solving the damage equation by Tikhonov regularization makes it possible to accurately determine damage extents in the case of incomplete modal data
A sensitivity-based finite element model updating based on unconstrained optimization problem and regularized solution methods
No full textAn effective and reliable approach to updating finite element (FE) models of real structures is to utilize a sensitivity-based strategy. A challenging issue concerning the sensitivity-based finite element model updating (FEMU) is to create a well-established framework for updating the inherent structural properties of FE models under incomplete noisy modal data. When noise contaminates the measured modal parameters, another challenging issue stems from the ill-posedness of the FEMU inverse problem. This article proposes an innovative sensitivity-based FEMU strategy based on the combination of modal kinetic energy and modal strain energy for simultaneously updating the element mass and stiffness matrices of FE models. The great novelty of this strategy is to get an idea from the unconstrained optimization problem for the establishment of a sensitivity-based FEMU framework. The correction of the element mass and stiffness matrices in a simultaneous way is another novelty of the proposed FEMU strategy. Moreover, new iterative and hybrid regularization methods under the Krylov subspace theory and bidiagonalization process are presented to solve the ill-posed inverse problem of FEMU. The accuracy and reliability of the proposed methods are numerically validated by a two-story concrete frame and a two-span continuous steel truss along with some comparative analyses. Results demonstrate that the suggested sensitivity-based strategy and regularized solution methods are influential and successful in FEMU under incomplete noisy modal data
Damage detection by updating structural models based on linear objective functions
No full textThe objective of this article is to detect the location and severity of structural damage according to direct model updating of physical properties by a Moore-Penrose inverse problem. The proposed method is based on expanding the dynamic orthogonality conditions in a damaged structure for attaining the difference between physical properties of undamaged and damaged structures. Hence, a two-stage damage detection process consisting of localization and quantification of damage is established by using linear objective functions which are applied in the expanded orthogonality conditions. Thus, an error matrix which is characterized as damage index is determined to identify the location of damage. Subsequently, damage extent is quantified by applying a linear objective function in the extended eigenproblem of the damaged structure. Eventually, two numerical examples are utilized to validate the proposed damage detection approach. In these examples, the modal data are considered to be incomplete and the inverses of rectangular matrices are accomplished by the Moore-Penrose technique while no multipliers are used. Furthermore, in all damage investigations, the predicted damage is compared with the preset values of induced damage. It can be concluded that the damage localization approach proposed in this study can precisely identify the location of damage through updating process. Moreover, the obtained results confirm this technique as being appropriate to predict the severity of damage. © 2014 Springer-Verlag Berlin Heidelberg
Improving feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods
Get PDFFeature extraction by time series modeling based on statistical pattern recognition is a powerful approach to Structural Health Monitoring (SHM). Determination of an adequate order and identi cation of an appropriate model play prominent roles in extracting sensitive features to damage from time series representations. Early damage detection under statistical decision-making via high-dimensional features is another signi cant issue. The main objectives of this study were to improve a residual-based feature extraction method by time series modeling and to propose a multivariate data visualization approach to early damage detection. A simple graphical tool based on Box-Jenkins methodology was adopted to identify the most compatible time series model with vibration time-domain measurements. Furthermore, k-means and Gaussian Mixture Model (GMM) clustering techniques were utilized to examine the performance of the residuals of the identi ed model in damage detection. A numerical concrete beam and an experimental benchmark model were applied to verifying the improved and proposed methods along with comparative analyses. Results showed that the approaches were successful and superior to a state-of-the-art order determination technique in obtaining a sufficient order, generating uncorrelated residuals, extracting sensitive features to damage, and accurately detecting early damage by high-dimensional data
Non-parametric empirical machine learning for short-term and long-term structural health monitoring
No full textEarly damage detection is an initial step of structural health monitoring. Thanks to recent advances in sensing technology, the application of data-driven methods based on the concept of machine learning has significantly increased among civil engineers and researchers. On this basis, this article proposes a novel non-parametric anomaly detection method in an unsupervised learning manner via the theory of empirical machine learning. The main objective of this method is to define a new damage index by using some empirical measure and the concept of minimum distance value. For this reason, an empirical local density is initially computed for each feature and then multiplied by the minimum distance of that feature to derive a new damage index for decision-making. The minimum distance is obtained by calculating the distances between each feature and training samples and finding the minimum quantity. The major contributions of this research contain developing a novel non-parametric algorithm for decision-making under high-dimensional and low-dimensional features and proposing a new damage index. To detect early damage, a threshold boundary is computed by using the extreme value theory, generalized Pareto distribution, and peak-over-threshold approach. Dynamic and statistical features of two full-scale bridges are used to verify the effectiveness and reliability of the proposed non-parametric anomaly detection. In order to further demonstrate its accuracy and proper performance, it is compared with some classical and recently published anomaly detection techniques. Results show that the proposed non-parametric method can effectively discriminate a damaged state from its undamaged condition with high damage detectability and inconsiderable false positive and false negative errors. This method also outperforms the anomaly detection techniques considered in the comparative studies
Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance
No full textDamage localization of damaged structures is an important issue in structural health monitoring. In data-based methods based on statistical pattern recognition, it is necessary to extract meaningful features from measured vibration signals and utilize a reliable statistical technique for locating damage. One of the challenging issues is to extract reliable features from non-stationary vibration signals caused by ambient excitation sources. This article proposes a new energy-based method by using ensemble empirical mode decomposition and Mahalanobis-squared distance to obtain energy-based multivariate features and locate structural damage under ambient vibration and non-stationary signals. The main components of the proposed method include extracting intrinsic mode functions of vibration signals by ensemble empirical mode decomposition, choosing adequate and optimal intrinsic mode functions, partitioning the selected intrinsic mode functions at each sensor into segments with the same dimensions, calculating the intrinsic mode function energy at each segment, preparing energy-based multivariate features at each sensor, computing Mahalanobis-squared distance values, and obtaining a vector of average Mahalanobis-squared distance quantities of all sensors. The major contributions of the proposed method consist of proposing an innovative non-parametric strategy for feature extraction, presenting generalized Pearson correlation function for the selection of optimal intrinsic mode functions, using a simple and effective segmentation algorithm, and applying energy-based features to the process of damage localization. The main advantage of the proposed method is its great applicability to locating single and multiple damage cases. The measured vibration responses of the well-known IASC-ASCE structure are applied to verify the effectiveness and reliability of the proposed energy-based method along with several comparative studies. Results will demonstrate that this approach is highly capable of locating damage under stationary and non-stationary vibration signals attributable to ambient excitations
Probabilistic damage localization by empirical data analysis and symmetric information measure
No full textThis article aims to address some challenges in data-based damage localization by proposing innovative methods including a hybrid algorithm for feature extraction, a symmetric information measure for feature classification, and a probabilistic approach to threshold estimation. The hybrid feature extraction combines an autoregressive (AR) model with a novel non-parametric estimator of probability density function (PDF) under empirical data analysis. The great novelty of this method is to propose a new probabilistic feature as an empirical PDF of the AR coefficients. The proposed information measure is a symmetric divergence aiming at addressing the main limitation of the classical Kullback-Leibler divergence regarding its non-symmetric characteristic. Finally, the proposed probabilistic approach exploits the concept of Markov Chain Monte Carlo to estimate a trustworthy threshold for locating damage. Vibration responses of two civil structures are used to verify the proposed methods with several comparisons. Results confirm that the methods are successful in identifying damage
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