Approach For Structural Damage Detection Research Articles

Structural damage identification under variable environmental/operational conditions based on singular spectrum analysis and statistical control chart

The influence of varying environmental/operational situations on damage-sensitive properties is a vital problem when the vibration responses are employed for structural damage detection. In this work, singular spectrum analysis (SSA) and the statistical control chart are combined to develop a new damage identification approach for structural damage detection under variable environmental/operational situations. The SSA is utilized to decompose the measured frequency sequence into the sum of independent components, including a tardily changing trend, oscillatory, and noise components. The trend-related decomposed components can be identified by finding the corresponding slow-varying eigenvectors. The trend components induced by varying environmental/operational conditions are discarded, while the remaining ones, including oscillatory and noise components, are selected to reconstruct the new frequency sequence. Hence, the impact of variable environmental/operational situations on the original frequency sequence is removed. The reconstructed frequency shift from its reference status (undamaged status) is utilized to construct the damage features, which are employed as the control chart samples. Accordingly, the damage features obtained from the undamaged status are utilized to calculate the control limits. The subsequent damage indices in unknown status are monitored considering the control limits. A considerable number of damage indices out of the control limits range show a system transformation from an undamaged status to a damaged one. The feasibility of the presented approach is analytically and experimentally evaluated through an offshore platform in the presence of a white noise excitation.

Guided Water Strider Algorithm for Structural Damage Detection Using Incomplete Modal Data

Over the last decades, metaheuristic algorithms have become versatile tools for solving various engineering optimization problems. That is why their application for structural damage identification has attracted the attention of researchers in civil engineering. In this paper, a guided structural damage detection approach using the recently developed optimization algorithm, water strider algorithm (WSA), is applied to detect multiple damage cases in structures. To reduce the computational costs, modal strain energy as a sensitive characteristic is used to localize damage locations in structures and guide the optimization algorithm. Considering incomplete modal data, four numerical examples and two with-noise and noise-free scenarios are examined. The results indicate the applicability and accuracy of the WSA in structural damage detection compared to other algorithms.

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model

In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation (RMSD) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

An improved generalized flexibility matrix approach for structural damage detection

ABSTRACTStructural damage detection is a key part of structural health monitoring. The generalized flexibility matrix approach is an effective method for damage detection. Compared with the flexibility matrix approach, it requires less number of natural frequencies and corresponding mode shapes. In this paper, the approach is improved by considering the nonnegativity of the damage extent. The error bound of two solutions calculated by the improved approach using natural frequencies and corresponding mode shapes without and with noises is derived. Numerical examples with different damage scenarios show that compared with the original generalized flexibility matrix approach, our proposed method works better.

Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference

Structural damage identification can be considered as an optimization problem, by defining an appropriate objective function relevant to structural parameters to be identified with optimization techniques. This paper proposes a new heuristic algorithm, named improved Jaya (I-Jaya) algorithm, for structural damage identification with the modified objective function based on sparse regularization and Bayesian inference. To improve the global optimization capacity and robustness of the original Jaya algorithm, a clustering strategy is employed to replace solutions with low-quality objective values and a new updated equation is used for the best-so-far solution. The objective function that is sensitive and robust for effective and reliable damage identification is developed through sparse regularization and Bayesian inference and used for optimization analysis with the proposed I-Jaya algorithm. Benchmark tests are conducted to verify the improvement in the developed algorithm. Numerical studies on a truss structure and experimental validations on an experimental reinforced concrete bridge model are performed to verify the developed approach. A limited quantity of modal data, which is distinctively less than the number of unknown system parameters, are used for structural damage identification. Significant measurement noise effect and modelling errors are considered. Damage identification results demonstrate that the proposed method based on the I-Jaya algorithm and the modified objective function based on sparse regularization and Bayesian inference can provide accurate and reliable damage identification, indicating the proposed method is a promising approach for structural damage detection using data with significant uncertainties and limited measurement information.

Synthesis of Cross-Correlation Functions of Partial Responses and the Extended Kalman Filter Approach for Structural Damage Detection Under Ambient Excitations

It is still necessary to investigate the detection of structural damage under ambient excitations since the excitations are random and unmeasured while measurement noises are inevitable. In this paper, a method based on the synthesis of cross-correlation functions of partial structural responses and the extended Kalman filter (EKF) approach is proposed for the identification and damage detection of structures under ambient excitations, in which both independent stationary and non-stationary white noise excitations in the product models are discussed. First, the equations of cross-correlation functions of structural responses are established when the ambient excitations are independent stationary white noise processes. Then, the EKF approach is utilized to identify structural parameters and cross-correlation functions using partial measurements of structural acceleration responses. Structural damage is detected based on the degradations of the identified structural element stiffness parameters. Finally, the proposed method is extended to deal with independent non-stationary white noise excitations in the product models. The numerical simulation examples of the ASCE structural health monitoring benchmark building subject to ambient excitation, a moment resisting frame model under white noise excitation, and a cantilever beam model under multiple independent non-stationary excitations are used to validate the feasibility of the proposed method. It is shown that the method is not sensitive to measurement noises. Also, a lab experimental study of the identification of a multi-story shear structure is investigated to further illustrate the applicability of the proposed method.

Probabilistic structural damage detection approaches based on structural dynamic response moments

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

Bird mating optimizer for structural damage detection using a hybrid objective function

Abstract A structural damage detection approach based on bird mating optimizer (BMO) in time-frequency domain is proposed in this paper. A hybrid objective function is introduced by minimizing the discrepancies between the measured and calculated natural frequencies and correlation function vector of acceleration of damaged and intact structures. Then the BMO algorithm with a disturbance procedure is developed to solve the objective function. Benefited from the hybrid objective function, only a few number of natural frequencies are needed in the detection process. And the disturbance procedure designed in this paper can enhance the precision of identification. The efficiency and robustness of the proposed method are verified by a planar truss and a frame, a three connected shear buildings and an experimental work. The studies in numerical simulations validate that the proposed objective function and disturbance procedure are helpful to improve the precision of identification. The experimental work shows that the proposed method has the potential of practical application. In addition, comparison among the proposed method and other optimization algorithms, i.e. GA, ABC, L-SHADE and HCLPSO, reveals the superiority of the proposed method in structural damage detection.

Piezoelectric impedance based damage detection in truss bridges based on time frequency ARMA model

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

Reformulation of elemental modal strain energy method based on strain modes for structural damage detection

Structural damage detection method based on modal strain energy usually requires information at every degree of freedom. Due to the limited number of sensors and the difficulty in measuring rotational vibration, modal expansion is often adopted to match the degrees of freedom between the analytical and experimental models, which introduces errors. A novel modal strain energy–based structural damage detection approach is proposed in which the elemental modal strain energy is reformulated with strain modes. The method is introduced on an Euler–Bernoulli beam with uniform cross section. Only strain data are adopted in the newly proposed method and no rotational information of the structure is required. The numerical simulations and experimental validations are conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed method has a better performance than the modal strain energy–based structural damage detection approach with displacement mode and modal expansion technique.

A new approach for structural damage detection exploring the singular spectrum analysis

This article presents a novel approach for damage detection applied to structural health monitoring systems exploring the residues obtained from singular spectrum analysis. In this technique, a lead zirconate titanate patch acting as actuator excites the structure, and three other patches are used as sensors to receive the structural responses. This method is based on a high-frequency excitation range in order to overcome the problem caused when the low-vibration modes are excited. In this method, a wideband chirp signal, with low amplitude and variable frequency, is used to excite the structure. The response signals are acquired in the time domain, and the singular spectrum analysis procedure is performed. The residues obtained between the reconstructed and original time series are used to compute statistical metrics. The residues calculated from singular spectrum analysis are used to compute the root mean square deviation and correlation coefficient deviation metric indices, rendering the damage detection approach more reliable. Tests were carried out on an aluminum plate, and the results have demonstrated the effectiveness of the proposed method making it an excellent approach for structural health monitoring applications. The results exploring different numbers of components used during the reconstruction process of time series are obtained, and the highlights are presented.

A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function

Health assessment and monitoring of engineered systems have become one of the fastest growing multi-disciplinary research areas over the last two decades. One of the largest concerns in structural health monitoring is how to infer structural conditions from the measurements and the data collected by sensors. The ultimate aim is to detect the structural damages with a high level of certainty and hence to extend the life of structures. In this study, a new strategy for structural damage detection is proposed using least square support vector machines based on a new combinational kernel. Thin plate spline Littlewood–Paley wavelet kernel function introduced in this article is a novel combinational kernel function, which combines thin plate spline radial basis function kernel with local characteristics and a modified Littlewood–Paley wavelet kernel function with global characteristics. During the process of structural damage detection, a social harmony search algorithm optimizes the parameters of least square support vector machine and the thin plate spline Littlewood–Paley wavelet kernel. The results obtained by this method are compared with least square support vector machine based on the other combinational and conventional kernels. These results show that the accuracy of damage detection based on least square support vector machine with thin plate spline Littlewood–Paley wavelet kernel is higher than other methods that utilize conventional kernels under similar conditions. In comparison with other combinational kernels, least square support vector machine with thin plate spline Littlewood–Paley wavelet kernel possesses a better dissemination and learning ability by incorporating the advantages of radial basis function kernel and wavelet kernel functions.

Structural damage detection using artificial bee colony algorithm with hybrid search strategy

An approach for structural damage detection using the artificial bee colony (ABC) algorithm with hybrid search strategy based on modal data is presented. More search strategies are offered and the bee will choose one search mode based on the tournament selection strategy. And a kind of elimination mechanism is introduced to improve the convergence rate. A truss and a plate are studied as two numerical examples to illustrate the efficiency of proposed method. An experimental work on a beam is studied for further verification. Final results show the present method can acquire the better identification results, compared with those from GA, the original ABC and quick ABC (QABC) algorithm, even under some measurement noise.

Damage detection using self-powered wireless sensor data: An evolutionary approach

A major limitation in implementation of self-powered wireless sensors pertains to considerable loss of the sensed information. Consequently, interpretation of the limited but valued data generated by the self-powered wireless sensing technology becomes a challenging problem. To tackle this issue, this study presents an evolutionary computational approach for structural damage detection using the self-powered wireless sensor data. The proposed data interpretation system is based on the integration of a robust evolutionary technique, called gene expression programming (GEP), and finite element (FE) method. Several damage indicator variables are extracted upon the simulation of the compressed data stored in memory chips of self-powered sensors. For the analysis, the complicated case of gusset plate of bridge is considered. Different damage scenarios are introduced to the plate and for each scenario and sensor configuration, a damage detection model is derived. Bases on a logistic regression analysis, probabilities are assigned to each model to find the most probable damage state. The damage detection models are presented as MATLAB and Visual Basic codes for further analysis. An uncertainty analysis is performed through the contamination of the damage indicator features with different noise levels. The results indicate that the proposed method is efficiently capable of detecting different damage states in spite of high-level noise contamination.

An intelligent structural damage detection approach based on self-powered wireless sensor data

Abstract This study presents the results of an ongoing research project conducted by the U.S. Federal Highway Administration (FHWA) on developing an intelligent approach for structural damage detection. The proposed approach is established upon the simulation of the compressed data stored in memory chips of a newly developed self-powered wireless sensor. An innovative data interpretation system integrating finite element method (FEM) and probabilistic neural network (PNN) based on Bayesian decision theory is developed for damage detection. Several features extracted from the cumulative limited static strain data are used as damage indicator variables. Another contribution of this paper is to define indicator variables that simultaneously take into account the effect of array of sensors. The performance of the proposed approach is first evaluated for the case of a simply supported beam under three-point bending. Then, the efficiency of the method is tested for the complicated case of a bridge gusset plate. The beam and gusset plate structures are analyzed as 3D FE models. The static strain data from the FE simulations for different damage scenarios is used to calibrate the sensor-specific data interpretation algorithm. The viability and repeatability of the method is demonstrated by conducting a number of simulations. Furthermore, a general scheme is presented for finding the optimal number of data acquisition points (sensors) on the structure and the associated optimal locations. An uncertainty analysis is performed through the contamination of the damage indicator features with different Gaussian noise levels.

Hybrid two-step method of damage detection for plate-like structures

This paper develops a two-step approach for structural damage detection. In the first step, the two-dimensional generalized local entropy (2D-GLE) is proposed to identify the damage location and number for plates. Based on the 2D-GLE, singularities in plates are revealed with the aid of mode shapes; hence, damages are localized, and the number of which is estimated. Besides, the statistical analysis is applied in the analysis of 2D-GLE damage maps to overcome the difficulty in damage detection methods without baseline data. In the second step, the damage severities at the identified locations are evaluated via the optimization using artificial bee colony (ABC) with the designed frequency object function. The advantages of the present method are as follows: (1) No-baseline in the measurement of mode shapes. Damage localization via the 2D-GLE avoids using healthy mode shapes serving as baselines in damage detection, and then only damaged mode shapes are required. (2) No down-sampling. Compared with the wavelet-based methods, no down-sampling enhances the algorithmic robustness and accuracy. (3) Efficiency. This hybrid two-step method shrinks the solving parameter domain sharply in optimization via the first step. Hence, only the severity parameters need to be investigated in ABC. (4) Robustness. ABC ensures the robustness of the proposed method. Different damage cases are investigated in simulations, single-damage/multiple-damage cases, and noise immunity tests demonstrate that the proposed method is effective and accurate in damage detection. At last, a simple experiment is given to verify the present algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

Damage Assessment in Structures Using Incomplete Modal Data and Artificial Neural Network

This paper presents a novel approach for structural damage detection and estimation using incomplete noisy modal data and artificial neural network (ANN). A feed-forward back propagation network is proposed for estimating the structural damage location and severity. Incomplete modal data is used in the dynamic analysis of damaged structures by the condensed finite element model and as input parameters to the neural network for damage identification. In all cases, the first two natural modes were used for the training process. The present method is applied to three examples consisting of a simply supported beam, three-story plane frame, and spring-mass system. Also, the effect of the discrepancy in mass and stiffness between the finite element model and the actual tested dynamic system has been investigated. The results demonstrated the accuracy and efficiency of the proposed method using incomplete modal data, which may be noisy or noise-free.

An application of impediography to the high sensitivity and high resolution identification of structural damage

In this study we explore the use of impediographic techniques to perform damage detection in plate-like metal structures. Impediography relies on the piezo-resistive coupling of the host structure to reconstruct high sensitivity and high resolution maps of the internal electrical conductivity. By exploiting localized strain perturbations generated via focused acoustic waves, the piezo-resistive coupling allows extracting a set of linearly independent boundary voltage data that drastically reduces the ill-conditioning of the inverse problem, therefore increasing the performance. The localized perturbation is achieved by leveraging the concept of frequency selective structure (FSS), that is a dynamically tailored structural element enabling the required acoustic focusing via vibration localization. Based on the FSS approach, the impediographic technique is numerically tested to investigate the performance of the combined approach for structural damage detection. The effects of practical implementation issues, such as limited perturbations and limited boundary data, are also explored.

A structural damage detection approach using train-bridge interaction analysis and soft computing methods

In this study, a damage detection approach using train-induced vibration response of the bridge is proposed, utilizing only direct structural analysis by means of introducing soft computing methods. In this approach, the possible damage patterns of the bridge are assumed according to theoretical and empirical considerations at first. Then, the running train-induced dynamic response of the bridge under a certain damage pattern is calculated employing a developed train-bridge interaction analysis program. When the calculated result is most identical to the recorded response, this damage pattern will be the solution. However, owing to the huge number of possible damage patterns, it is extremely time-consuming to calculate the bridge responses of all the cases and thus difficult to identify the exact solution quickly. Therefore, the soft computing methods are introduced to quickly solve the problem in this approach. The basic concept and process of the proposed approach are presented in this paper, and its feasibility is numerically investigated using two different train models and a simple girder bridge model.

New method for structural damage detection based on generalised displacement

In this paper, a new structural damage detection approach based on changes in the generalised displacement is presented. The generalised displacement is first introduced, its curvature and change are then used to detect structural damage location. Compared with the conventional approach using the original displacement, the generalised displacement method is more effective and is more robust to the noise factors in this new approach. Finally, a numerical example for two-span beam is used to illustrate the effectiveness of this proposed method.