Approach For Structural Damage Identification Research Articles

An output-only damage identification method based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction

The absence of excitation measurements may pose a huge challenge in the application of many damage identification methods since it is difficult to acquire the external excitations, such as wind load, traffic load. To deal with this issue, a novel output-only structural damage identification approach based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction with Bayesian inference regularization is developed. On the one hand, heterogeneous measurements (e.g., displacements, strains, accelerations) are rescaled and reconstructed with the aid of Bayesian inference regularization technique. Structural damages are identified by minimizing the discrepancies between the measured and reconstructed responses. On the other hand, to solve the optimization-based inverse problem, a reinforcement-aided evolutionary algorithm, named Q-learning hybrid evolutionary algorithm (QHEA), integrating Jaya algorithm, differential algorithm, and Q-learning algorithm is proposed as search tool. To validate the feasibility and applicability of the proposed method, numerical studies on a three-span beam structure and laboratory tests on a five-story steel frame structure are carried out. The effects of data rescaling and data fusion on response reconstruction and damage identification are also investigated. The results clearly demonstrate the superiority of QHEA over other heuristic algorithms and heterogeneous data fusion over a single type of measurement. It is shown that both the locations and extents of the damaged elements can be accurately identified by the proposed method without the information of input force.

A Novel Mayfly Algorithm with Response Surface for Static Damage Identification Based on Multiple Indicators

This paper proposes a novel structural damage identification approach coupling the Mayfly algorithm (MA) with static displacement-based response surface (RS). Firstly, a hybrid optimal objective function is established that simultaneously considers the sensitivity-based residual errors of static damage identification equation and the static displacement residual. In the objective function, the static damage identification equation is addressed by the Tikhonov regularization technique. The MA is subsequently employed to conduct an optimal search and pinpoint the location and intensity of damages at the structural element level. To handle the inconformity of the static loading points and the measurement points of displacements, the model reduction and displacement extension techniques are implemented to reconstruct the static damage identification equation. Meanwhile, the static displacement-based RS is constructed to calculate the displacement residual in the hybrid objective function, thereby circumventing the time-consuming finite element calculations and improving computational efficiency. The identification results of the numerical box girder bridge demonstrate that the proposed method outperforms the particle swarm optimization, differential evolution, Jaya and whale optimization algorithms about both convergence rate in optimal searching and identification accuracy. The proposed method enables more accurate damage identification compared to methods solely based on the indicator of the residual of static damage identification equations or displacement residual. The results of identifying damage in the 21 element-truss structure and the static experiments on identifying damage in an aluminum alloy cantilever beam confirm the high efficiency of the proposed approach.

An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

A Novel Improved Dragonfly Algorithm-Based Structural Damage Identification Approach Using Flexibility Assurance Criterion and Trace Lasso

Aiming to enhance the accuracy, stability, and noise robustness of swarm intelligence-based algorithms for structural damage identification (SDI), a novel improved dragonfly algorithm (IDA) is proposed. The IDA integrates the dragonfly algorithm (DA) with three key strategies including enhanced Lévy flight, optimal solution bidirectional search, and greedy preservation. These strategies are introduced to enhance the exploration capability of the original DA and improve the IDA’s capacity to obtain global optima. An objective function is defined using frequency change ratio and flexibility assurance criterion (FAC). Additionally, trace sparse regularization is also incorporated into the objective function since most of the damages in structures tend to be sparsely distributed, so that a sparse result is ensured to improve SDI accuracy. To evaluate the performance of the proposed algorithm, a comparison of the original DA and IDA is conducted using four benchmark functions. The results demonstrate that the proposed algorithm achieves improved convergent rate and accuracy. Furthermore, numerical simulations are performed on a 10-element simply-supported beam and a 31-element planar truss to validate the effectiveness and efficiency of the proposed algorithm in SDI. Significantly, the utilization of IDA instead of DA leads to a substantial reduction in the average calculated relative error for the truly damaged element within the considered damage cases of the simply-supported beam, decreasing from 13.05% to 6.15%. Moreover, an experimental simply-supported beam structure with several assumed damage cases is fabricated in the laboratory. The experimental results further confirm the robustness and capability of the proposed method in real-world SDI applications.

Integration of measured data for the seismic fragility assessment of deteriorated bridges

AbstractThe reliable assessment of bridge capacity is necessary in order to ensure the functionality of the roadway network under normal, or emergency operating conditions. Damage due to the interaction with the environment (e.g., corrosion of coastal reinforcement) has been observed in many cases, even in newly built interurban road networks. Therefore, the need for obtaining and including measured data in fragility assessment analysis has emerged. In line with the above, the proposed research introduces a framework for the assessment of bridges considering integrating data obtained with NDTs. The scope of NDTs is to track down the evolution damage on bridges and determine its risk of failure. The work presents a structural damage identification approach that successfully updates a digital twin of the structure. Subsequently the digital twin is used in order to determine the bridge fragility. Fiber modeling is used for creating the digital twin of the structure. Modified uniaxial material laws are applied for the fragility assessment of a deteriorated case study bridge also providing comparative assessment results for the pristine bridge, i.e. without damage.

Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN

In order to improve the identification accuracy of damage detection and evaluation based on the vibration response, this paper presents a structural damage identification method based on the fractal dimension, data fusion and a revised counter-propagation network (RCPN). Firstly, the fractal dimensions of the original signal response are extracted through data preprocessing. Secondly, the first-time fusion of data (i.e., the feature-level fusion) is carried out, after which these data are used as the input for the RCPN, to identify and decide the initial damage. Finally, the second-time data fusion (i.e., based on the decision results of the feature-level fusion) is carried out, leading to decision-level fusion. In order to verify the validity of the proposed method, a four-storey benchmark structure of ASCE is used for damage identification and comparison, using a single RCPN decision and the data fusion damage identification method, respectively. The results show that the proposed method is more accurate and reliable than the results of single RCPN decision and feature-level fusion decision, and has good noise resistance and robustness.

A data-driven structural damage identification approach using deep convolutional-attention-recurrent neural architecture under temperature variations

In recent years, by emerging deep learning (DL) based algorithms, researchers have been exploring dl-based models to identify structural damage through data-driven approaches. dl-based data-driven techniques using autonomous feature extraction from raw sequential data are more robust under environmental variations. Extraction of robust features while considering the sequential dependencies will significantly improve the accuracy of damage identification by these techniques. In this regard, various architectures of dl-based models have been proposed. This study presents a novel dl-based model that utilizes both one-dimensional convolutional neural network (1DCNN) and recurrent neural network (RNN) variants using an attention mechanism. Attention mechanism improves the performance of the 1DCNN-RNN variants model precisely when its input data is raw acceleration time-history, as a kind of sequential data. The IASC-ASCE phase II and Qatar University grandstand simulator benchmarks are used to evaluate the proposed model by comparing its performance with dl-based neural network architectures that could be equivalent to this combination. Moreover, the environmental variable which affects structural response is also examined. Results demonstrate that the CNN-ATT-biGRU model architecture has the best accuracy and appropriate training time and model size among nine compared architectures.

Data-driven identification of structural damage under unknown seismic excitations using the energy integrals of strain signals transformed from transmissibility functions

In recent years, the methodology based on the energy variations of structural dynamic strain responses has been developed. Although it is an efficient data-driven structural damage identification approach, it can only be used for structures under impulse or known excitations. To overcome this limitation, an improved method is proposed in this paper for the rapid damage identification of beam-like structures using structural dynamic strain responses under unknown seismic excitations. The influence of different excitations to an intact and damaged structure, in which the intact structure is under ambient excitations and structural damage is caused by unknown seismic excitations, is eliminated via the transmissibility functions (TFs) of measured structural strain responses. Contrary to the previous TFs of structural acceleration or displacement responses, TFs of structural strains is more sensitive to local damage in beam-like structures. Moreover, power spectral density transmissibility functions (PSDTFs) are used instead of the traditional Fourier transform TFs. Then, through the inverse Fourier transformation in the frequency domain, the PSDTFs are returned to the time-domain signals to avoid the problem of selecting the proper frequency band in previous studies on identifying structural damage based on TFs in the frequency domain. Finally, structural damage can be identified based on the energy variations of the time-domain strain signals from the intact and damaged structures. Numerical examples of structural damage identification of a seven-storey planar frame and a cable-stayed bridge girder under unknown seismic excitations are provided to demonstrate the effectiveness and robustness of the improved method. Moreover, structural damage identification of a laboratory cable-stayed bridge tower model subjected to shaking table tests is performed to validate the good performance of the proposed method.

Inverse Analysis of Structural Damage Based on the Modal Kinetic and Strain Energies with the Novel Oppositional Unified Particle Swarm Gradient-Based Optimizer

Structural damage inspection is a key structural engineering technique that strives for ensuring structural safety. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using evolutionary computation. By considering the recent advances in this field, an efficient hybrid objective function that combines the global modal kinetic and modal strain energies is introduced. The newly developed objective function aims to extract maximum dynamic information from the structure and overcome noisy conditions. Moreover, the original methods are usually vulnerable to the associated high multimodality and uncertainty of the inverse problem. Therefore, the oppositional learning (OL) for population initialization and convergence acceleration is first adopted. Thereafter, the unified particle swarm algorithm (UPSO) mechanism is combined with another newly developed algorithm, the gradient-based optimizer (GBO). The new algorithm, called the oppositional unified particle swarm gradient-based optimizer (OL-UPSGBO), with the convergence acceleration feature of (OL), enhances balanced exploration-exploitation of UPSO, and the local escaping operator of GBO is designed to specifically deal with the complex inverse analysis of structural damage problems. To authenticate the performance of the OL-UPSGBO, the complex benchmark set of CEC 2017 is adopted to compare the OL-UPSGBO with several original metaheuristics. Furthermore, the developed approach for structural damage identification is tested using several damage scenarios in a multi-story frame structure. Results show that the developed approach shows superior performance and robust behavior when tackling the inverse analysis of structural damage.

Damage identification in beam-like structure using strain FRF-based damage index and artificial neural network

A novel approach for structural damage identification of beam-like structures based on strain frequency response functions (SFRFs) using artificial neural networks (ANNs) is proposed. The dominated singular values of the phase space matrix derived from SFRFs are reserved as the features to denote the characteristics of the original SFRFs. And the reserved singular values mean change ratio (RSVMCR) is described as a new damage index for damage detection. The RSVMCRs and the elemental residual stiffness vector are utilized to establish the ANN model. Two numerical examples are investigated to verify the feasibility and effectiveness of the proposed approach.

A two-step interval structural damage identification approach based on model updating and set-membership technique

External conditions unavoidably cause deterioration to structures in service, and the degeneration in material property may accumulate and lead to huge accidents. Meanwhile, practical engineering includes a variety of uncertainties from responses, which can negatively influence the damage identification result. In this study, a two-step interval structural damage identification approach is proposed with consideration of response uncertainty. Deterministic damage identification strategy by which damage index can be solved is firstly introduced. Then, damage locations are identified as the first step based on model updating. For the second step, uncertain structural dynamic responses are viewed as interval variables. Then, the bounds of the damage extents of the identified elements are estimated using Chebyshev polynomial approximation and interval mathematics. For a tighter solution, set-membership technique is used to obtain the fusion interval damage index. Two numerical verifications and an experimental verification show the use of the presented two-step identification method. In addition, the Taylor expansion and interval combination methods are also analyzed for comparable purpose.

A novel structural damage identification approach using damage-induced perturbation in longitudinal vibration

Structural damage identification approaches relying on structural vibration shapes (VSs) have been widely developed. Among the approaches, the pseudo-force approach has attracted increasing attention during the recent decade owing to the fact that the damage-induced pseudo-force is concentrated within the damage region only and rapidly attenuates at undamaged locations. Transverse pseudo-forces (TPFs) relying on flexural VSs have been used for structural damage identification. However, the TPF approach is inapplicable to some structures governed mainly by tension and not bending, such as cables in a cable-stayed bridge, because bending effects on their flexural vibrations are much smaller compared to their tension effects. In contrast, longitudinal VSs can be useful for identifying such damages, although they are much more difficult to measure than flexural VSs. In this study, a new concept of axial pseudo-force (APF) is formulated using damage-induced perturbation in longitudinal vibration, which forms the basis of a novel damage identification approach for longitudinally vibrating structures. Unlike the TPF approach relying on transverse bending, the proposed APF approach relies on axial tension/compression. In particular, a damage index (DI) is established to indicate and locate structural damage. The multiscale analysis is integrated into the DI to enhance its robustness against environmental noise interference. A normalization strategy is further proposed to deal with unknown material and structural parameters in practical scenarios. The capability of the approach in identifying damage in longitudinally vibrating structures is analytically verified on bars with two-sided notches. The applicability of the approach is experimentally validated by identifying a two-sided notch in an aluminum bar whose longitudinal VSs were acquired through non-contact vibration measurement using a three-dimensional (3D) scanning laser vibrometer (SLV).

Sensor clustering-based approach for structural damage identification under ambient vibration

This study explored the sensor clustering-based damage detection beyond the free-vibration limitation to allow for the direct utilisation of time-series for damage identification under ambient vibration. In the proposed method, a dense sensor network is clustered and each sensor cluster is represented by nonlinear autoregressive with exogenous inputs (NARX) model, which is developed in a black-box manner via an artificial neural network. Damage detection is achieved through a new damage sensitive feature which is formulated from the NARX neural network prediction error. The efficiency of the proposed methodology is assessed firstly using test data of an 8-DOF system and later by conducting an experimental study on scaled steel arch laboratory models subjected to various damage cases. The obtained results reveal that the proposed method can satisfactorily detect, localise, and estimate damage severity in the test structure.

Non-probabilistic method to consider uncertainties in structural damage identification based on Hybrid Jaya and Tree Seeds Algorithm

This paper proposes a novel non-probabilistic structural damage identification approach by developing a hybrid swarm intelligence technique based on Jaya and Tree Seeds Algorithm (TSA), taking into account the high-level uncertainties in the measurements and finite element modelling. The damage in structure is simulated as reduction of elemental stiffness, and structural damage identification is formulated as an optimization problem. To overcome the challenge for structural damage identification with a limited number of measurement data, an objective function based on the modal data and sparse regularization technique is defined. To make the optimization algorithm more powerful and robust, a hybridization of the K-means clustering based Jaya and TSA is proposed. Jaya algorithm is taken as the core in the hybridization. The clustering strategy is employed to replace solutions with low-quality objective values in the Jaya algorithm. Then the search strategy of the TSA is introduced into the best-so-far solution of each cycle. The proposed hybridization algorithm is termed as “ C-Jaya-TSA”. To enhance the capacity of the proposed algorithm to consider uncertainties, a non-probabilistic method is also integrated to calculate the interval bound (lower and upper bounds) of the elemental stiffness changes by using the interval analysis method. To better quantify the structural damage extents, Damage Measure Index (DMI) values are introduced for representing structural damage states. The DMI value can be viewed as a combination of deterministic stiffness reduction and the Possibility of Damage Existence (PoDE). Numerical benchmark functions, numerical studies and experimental investigations are conducted to verify the accuracy and performance of the proposed method. The identification results show that the developed C-Jaya-TSA integrated with the non-probabilistic interval analysis method is a promising tool to accurately identify the structural damage, even high-level uncertainties exist.

A Shannon entropy approach for structural damage identification based on self-powered sensor data

Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions.

A nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics

In this study, a nonprobabilistic structural damage identification approach based on orthogonal polynomial expansion and interval mathematics is proposed for uncertainty-oriented damage identification with insufficient parametric information. A deterministic damage identification method is firstly reviewed. By means of a nonnegative least squares algorithm that combines truncated singular value decomposition and the L-curve method, the ill-posed damage equation is then solved, and the damage index is simultaneously obtained. In terms of the uncertainty quantification issue, the material elastic modulus and density in structural models herein are described as unknown-but-bounded interval numbers. On the basis of orthogonal polynomial expansion and interval mathematics, the set collocation methodology is presented to determine the upper and lower bounds of the damage index. The interval bounds can provide supports for structural health diagnosis under uncertain conditions. A nonprobabilistic identification using the first-order Taylor series expansion is also proposed for comparison's purpose. Two numerical applications and one test are finally given under single damage and multidamage scenarios with different uncertainty degree. Results suggest that the presented nonprobabilistic structural damage identification approach can identify the damage location and degree with consideration of uncertainties.

Structural damage identification with uncertain modelling error and measurement noise by clustering based tree seeds algorithm

This paper proposes a novel structural damage identification approach by using the clustering based Tree Seeds Algorithm, termed as C-TSA, taking into account of both the finite element modeling errors and measurement noise. In order to make the standard TSA more powerful and robust, K-means cluster technique is introduced into the standard TSA before starting the seeds search, which is beneficial to enhance the algorithm’s global optimization performance. The objective function based on the modal data is formulated for structural damage identification. Numerical studies on benchmark functions and a 61-bar truss structure are conducted to investigate the accuracy and robustness of the proposed approach. The finite element modelling errors and noises in the measurement data are considered. Experimental verifications on a laboratory steel frame structure model is conducted to further validate the accuracy of the proposed approach. The results from the numerical and experimental studies are compared with those obtained from several latest evolutionary algorithms. The identification results demonstrate that the proposed approach is more competitive and robust for structural damage identification even considering the modelling errors and measurement noises.

Iterative two-stage approach for identifying structural damage by combining the modal strain energy decomposition method with the multiobjective particle swarm optimization algorithm

An iterative two-stage structural damage identification approach that combines the modal strain energy decomposition (MSED) method with the multiobjective particle swarm optimization algorithm is presented. The proposed scheme is inspired by the general noniterative two-stage approach, which attempts to locate damage in the first stage and estimate the severity of damage in the second stage; however, the present approach differs by constructing an iterative MSED indicator to perform damage localization and severity estimation in a cyclic manner. Consequently, false positive indications of damage are gradually suppressed, and the precision of the damage severity estimation is improved as the iteration continues. In addition, a new element activation operation is embedded into the iterative process; in this step, possible false negative indications of damage excluded in the first stage can be activated and subsequently retested to identify damage, thereby reducing the false negative rate. Furthermore, a comparative study is conducted between the iterative two-stage approach and the general noniterative approach by contemplating a 3-D structure, and the effects of measurement noise and spatial incompleteness are considered.

Impedance resonant frequency sensitivity based structural damage identification with sparse regularization: experimental studies

Electromechanical impedance (EMI) based structural health monitoring methods have been successfully applied to various engineering fields. However, the studies on damage quantification using EMI based techniques are still limited. In general, conventional EMI based methods evaluate the changes in the host structure by comparing the difference between impedance responses from undamaged and damaged structures, with statistical damage indicators, i.e. root mean square deviation (RMSD) and cross correlation. These damage indicators can detect the existence of damage in structures, but are not able to precisely locate and identify the severity of damages. This paper presents experimental validations on a novel structural damage identification approach based on the sensitivity of resonance frequency shifts in the impedance and sparse regularization technique. The coupled finite element model of the piezoelectric transducer and host structure is developed and calibrated for the damage quantification. A limited number of measured resonance frequency shifts are used to identify the damage in a number of segments in the host structure. Experimental verifications are conducted on narrow aluminum plates to demonstrate the accuracy and performance of the presented approach. The identification results demonstrate the effectiveness and performance of using the proposed approach for structural damage localization and quantification. To investigate the capacity of impedance based technique for SHM with the proposed approach, numerical studies are further conducted to discuss the sensitivity range of this method.

Deep principal component analysis: An enhanced approach for structural damage identification

The structural health monitoring relies on the continuous observation of a dynamic system over time to identify its actual condition, detect abnormal behaviors, and predict future states. The regular changes in environmental factors have been reported as one of the main challenges for the application of structural health monitoring systems. These influences in the structural responses are in general nonlinear, affecting the damage-sensitive features in the most varied forms. The usual process to remove these normal changes is referred to as data normalization. In that regard, principal component analysis is probably the most studied algorithm in structural health monitoring, having numerous versions to learn strong nonlinear normal changes. However, in most cases, not all variability is properly accounted for via the existing nonlinear principal component analysis approaches, resulting in poor damage detection and quantification performances. In this article, a new paradigm based on deep principal component analysis, rooted in the deep learning field, is presented to overcome these limitations. This approach extracts the most salient underlying feature distributions by stacking multiple feedforward neural networks trained to learn an identity mapping of the input variables, where the network inputs are reproduced into the outputs. Similar to the traditional nonlinear principal component analysis–based approach, our approach identifies a nonlinear output-only model of an undamaged structure by comprising modal features into an internal bottleneck layer, which implicitly represents the independent environmental factors. The proposed technique is validated through the application on a progressively damaged prestressed concrete bridge and a three-span suspension bridge. The experimental results demonstrate that capturing the most slight nonlinear variations in the data can lead to improved data normalization and, consequently, better damage detection and quantification performances.