Structural Damage Localization Research Articles

Self-diagnosis of structural damage in self-powered piezoelectric composites

At present, nondestructive testing and non-self-powered structure health monitoring methods are usually used to judge the damage of fiber reinforced composites. However, these methods have some limitations due to the lack of real-time characteristics, the need for external power supply or the complex structure of sensor. In this work, the piezoelectric nanogenerator mechanism was integrated into the traditional composites to develop the piezoelectric composite with damage self-diagnosis function. The piezoelectric composites can realize structural health monitoring and damage localization without reducing the original mechanical properties. The effects of PVDF and barium titanate on the mechanical properties of the composites were investigated by experiments and simulations. Furthermore, the damage self-diagnosis characteristics of composites under bending load and impact load were studied. The algebraic relationship between the damage area and the open circuit voltage under impact load was established. In addition, the damage localization of composite materials is realized by designing an Application (APP). In general, this work has successfully set a precedent for the application of nanogenerator mechanism to the self-powered and self-diagnosis of composite damage, which has the potential to be used in any field related to composites, such as aerospace, etc.

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

In recent years, the development of quick and streamlined methods for the detection and localization of structural damage has been achieved by analysing key dynamic parameters before and after significant events or as a result of aging. Many Structural Health Monitoring (SHM) systems rely on the relationship between occurred damage and variations in eigenfrequencies. While it is acknowledged that damage can affect eigenfrequencies, the reverse is not necessarily true, particularly for minor frequency variations. Thus, reducing false positives is essential for the effectiveness of SHM systems. The aim of this paper is to identify scenarios where observed changes in eigenfrequencies are not caused by structural damage, but rather by non-stationary combinations of input and system response (e.g., wind effects, traffic vibrations), or by stochastic variations in mass, damping, and stiffness (e.g., environmental variations). To achieve this, statistical variations of thresholds were established to separate linear non-stationary behaviour from nonlinear structural behaviour. The Duffing oscillator was employed in this study to perform various nonlinear analyses via Monte Carlo simulations.

Structural damage localisation based on generalised flexibility change rate curvature and data fusion technology

ABSTRACT Damage localisation is the key to structural damage identification. Based on the study of damage index using the flexibility method, this paper presents a damage localisation index using the generalised flexibility matrix before and after structural damage. To enhance the accuracy of structural damage localisation, it is necessary to improve the sensitivity of damage index. Therefore, this paper puts forward a generalised flexibility change rate curvature index with the objective of improving the accuracy in localising structural damage. The curvature index is defined as the second-order difference of the generalised flexibility change before and after damage. Consequently, the curvature-based index usually is more sensitive to structural damages. To enhance the accuracy and efficiency of multiple damage localisation, the generalised flexibility change rate curvature index is combined with data fusion technology based on Dempster-Shafer’s theory. Numerical simulations and experimental studies have validated the capability and efficiency of the proposed method for damage localisation also in the case of multiple damages, noise impact, and limited modal orders.

Identifying damage in long-span self-anchored suspension bridges using characteristic indices

This study presents a methodology for detecting damage in bridge structures using three damage identification indices: the deviation of displacement influence line (DDIL), the deviation curvature of displacement influence line (DCDIL), and the relative rate of change of wavelet packet energy (RES). The proposed approach is based on the principles of displacement influence line and wavelet packet transform. To demonstrate the effectiveness of the three metrics in determining the location of structural damage, a finite element model of the main bridge in Zhengzhou Taohuayu Yellow River Bridge, a three-span twin-tower all-steel girder self-anchored suspension bridge, is evaluated. The study investigates the impact of various factors, such as damage degree, damage location, signal noise, variable speed vehicles, and random traffic flow, on the usefulness of the metrics. The results indicate that all three indices are effective in detecting damage in bridge structures. The DCDIL demonstrates a distinct, abrupt change at the location of damage, while the DDIL is somewhat less effective. Additionally, the RES curve is particularly sensitive to local damage and can identify damage locations far from the bridge span. However, the study notes that the accuracy and reliability of the RES identification indicators decrease non-linearly in the presence of signal noise. The highest values of the RES curve are positively correlated with acceleration and less closely related to uniform acceleration. A vehicle speed of 20 m/s is recommended for identifying the location of bridge structure damage. Finally, the study suggests that random, unevenly spaced traffic enhances vibration and can more precisely localize the damage location and degree of the bridge structure.

Research on Bridge Integrity Assessment and Early Warning Monitoring Methods Based on Bearing Reaction Force

Traditional bridge monitoring and damage identification techniques typically rely on full-bridge coverage of sensors, such as displacement or strain sensors. However, this approach proves economically unfeasible for the vast numbers of small- and medium-span continuous beam bridges. In response to the need for rapid damage identification and integrity assessment of continuous beam bridges, a novel bridge safety monitoring method relying solely on bearing reaction forces is proposed. Firstly, the analytical expressions for the bearing reaction influence lines of a three-span continuous beam bridge under damage conditions were derived. Secondly, a rapid structural damage localization method based on the bearing reaction influence lines was proposed. Finally, feasibility and applicability were confirmed through numerical simulations and experimental validation. Additionally, the discussion includes the implementation of the warning classification and threshold setting using data from bearing force monitoring. The research demonstrates that utilizing a limited amount of bearing reaction force information can not only identify damage areas in a “non-full-coverage” manner, but also facilitates early warning and the integrity assessment of bridges. In the future, there is potential for large-scale application in medium- and small-span continuous beam bridges.

Vibration-based structural damage localization through a discriminant analysis strategy with cepstral coefficients

Over the past decades, Vibration-Based Methods (VBMs) have consistently exhibited exceptional effectiveness in the field of Structural Health Monitoring when it comes to assessing structural damage in both civil and mechanical structures. Recently, the progress made in data-driven strategies for localizing structural damage through the VBMs has resulted in substantial benefits. These advanced strategies not only enable an efficient decision-making process but also provide precise identification of repair locations for monitored structures. Importantly, they achieve this without the inherent complexity and computational burden typically associated with traditional model-based methods. In this study, an innovative data-driven method for localizing and quantifying structural damage is proposed. The method is developed on the basis of the principles of Linear Discriminant Analysis (LDA) and a newly devised modeling strategy that utilizes the power cepstral coefficients extracted from the structural acceleration response. The developed LDA model is able to highlight the local structural characteristics (i.e., mode shape-related information) embedded in the cepstral coefficients in the LDA latent space. Based on the highlighted local characteristics of the cepstral coefficients, a statistical pattern recognition strategy is proposed to effectively conduct the quantification and localization of structural damage. The proposed damage localization method is designed to function in a completely unsupervised-learning manner, which eliminates the requirement for the model to have access to any prior knowledge or information regarding structural damage during the training phase. The proposed method has been validated by both numerical simulations and experimental data.

Baseline-free damage localization of structures under unknown seismic excitations based on strain transmissibility and wavelet transform of strain mode

The necessity of baseline data in current damage detection methodologies limits their applications in practice, so it is essential to implement baseline-free damage detection of structures. In this paper, a baseline-free damage detection method is proposed based on the combination of structural strain transmissibility and wavelet transform of strain mode shape. First, the strain mode shape of a structure is identified based on the transmissibility of structural strain responses under seismic excitation. Then, by considering the sensitivity of wavelet analysis to the abrupt-change signal, the strain mode shape abnormality at the damage location is amplified by wavelet transform to accomplish damage detection. Two numerical simulation examples for structural damage detection have demonstrated the proposed method. Moreover, a structural damage detection experiment of a lab cable-stayed bridge tower model under shaking table also verified the proposed method.

Magnetic resonance neurography in spinal cord injury: Imaging findings and clinical significance.

It is unknown whether changes to the peripheral nervous system following spinal cord injury (SCI) are relevant for functional recovery or the development of neuropathic pain below the level of injury. Magnetic resonance neurography (MRN) at 3 T allows detection and localization of structural and functional nerve damage. This study aimed to combine MRN and clinical assessments in individuals with chronic SCI and nondisabled controls. Twenty participants with chronic SCI and 20 controls matched for gender, age, and body mass index underwent MRN of the L5 dorsal root ganglia (DRG) and the sciatic nerve. DRG volume, sciatic nerve mean cross-sectional area (CSA), fascicular lesion load, and fractional anisotropy (FA), a marker for functional nerve integrity, were calculated. Results were correlated with clinical assessments and nerve conduction studies. Sciatic nerve CSA and lesion load were higher (21.29 ± 5.82 mm2 vs. 14.08 ± 4.62 mm2 , p < 0.001; and 8.70 ± 7.47% vs. 3.60 ± 2.45%, p < 0.001) in individuals with SCI compared to controls, whereas FA was lower (0.55 ± 0.11 vs. 0.63 ± 0.08, p = 0.022). DRG volumes were larger in individuals with SCI who suffered from neuropathic pain compared to those without neuropathic pain (223.7 ± 53.08 mm3 vs. 159.7 ± 55.66 mm3 , p = 0.043). Sciatic MRN parameters correlated with electrophysiological results but did not correlate with the extent of myelopathy or clinical severity of SCI. Individuals with chronic SCI are subject to a decline of structural peripheral nerve integrity that may occur independently from the clinical severity of SCI. Larger volumes of DRG in SCI with neuropathic pain support existing evidence from animal studies on SCI-related neuropathic pain.

A hybrid multiple input multiple output (MIMO) model for simultaneous localization and quantification of structural damage in beam structures

Minor cracks are common types of damage in beam-like structures that must be detected early to prevent complete structural failure. Detecting and addressing such damage is crucial for maintaining structural integrity, extending service life, and minimizing operational costs. In recent years, various methods for quantifying and localizing damage using vibration measurements have been developed. However, many of these methods struggle to quantify and accurately localize the damage simultaneously. This paper addresses this challenge by applying novel hybrid multi-input and multi-output (MIMO) regression algorithms. The proposed approach combines modal analysis data, which includes frequencies, with machine learning algorithms to achieve simultaneous damage localization and quantification in damaged beams. Both numerical and experimental modal analyses have been conducted to determine the first six modal frequencies. Multiple hybrid MIMO models have been trained and tested using the frequencies obtained. The trained models have been used to estimate damage location and severity, and the results are compared. The models’ performance was evaluated using seven key metrics, including mean squared error (MSE), coefficient of determination (R2), mean absolute error (MAE), and others. The Random Forest (RF) model demonstrated exceptional performance by achieving a significantly higher R2 value of 0.997 for the precise prediction of both damage location and magnitude. Optimizing these models through hyperparameter tuning significantly improved their accuracy and reliability. This approach exhibits superior efficacy, surpassing existing models, and holds promise for advancing structural health monitoring in critical applications.

Brain Structural and Functional Damage Network Localization of Suicide

BackgroundExtensive neuroimaging research on brain structural and functional correlates of suicide has produced inconsistent results. Despite increasing recognition that damage in multiple different brain locations that causes the same symptom can map to a common brain network, there is still a paucity of research investigating network localization of suicide. MethodsTo clarify this issue, we initially identified brain structural and functional damage locations in relation to suicide from 63 published studies with 2135 suicidal and 2606 nonsuicidal individuals. By applying novel functional connectivity network mapping to large-scale discovery and validation resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 suicide brain damage networks corresponding to different imaging modalities. ResultsThe suicide gray matter volume damage network comprised widely distributed brain areas primarily involving the dorsal default mode, basal ganglia, and anterior salience networks. The suicide task-induced activation damage network was similar to but less extensive than the gray matter volume damage network, predominantly implicating the same canonical networks. The suicide resting-state activity damage network manifested as a localized set of brain regions encompassing the orbitofrontal cortex and middle cingulate cortex. ConclusionsOur findings not only may help reconcile prior heterogeneous neuroimaging results, but also may provide insights into the neurobiological mechanisms of suicide from a network perspective, which may ultimately inform more targeted and effective strategies to prevent suicide.

Unsupervised structural damage assessment method based on response correlations

Structural damage assessment for civil engineering structures has become an important research topic and has gained much attention for decades in the field of structural health monitoring (SHM). Current unsupervised structural damage assessment methods can achieve damage detection for practical civil engineering structures. However, these methods cannot fully mine the response correlations among sensors, thus are difficult to localize damage in practical engineering applications. Therefore, this study proposes an unsupervised structural damage assessment method with bidirectional long short-term memory (BiLSTM) networks to establish the response correlations among sensors. Numerical studies of a beam-like bridge model, an experimental study of Yellow Frame, and an in-field study of Yonghe Bridge were conducted to validate the accuracy and reliability of the proposed approach. The results show that unsupervised structural damage identification and localization can be achieved even in the case of sparse sensor deployment due to the fact that the response correlations among sensors can be well extracted. The proposed approach can be applied to damage identification of practical engineering structures under operational and ambient excitations and can automatically provide damage location warnings to ensure structural health and prevent catastrophic events.

A 2D-CNN-Based Two-Stage Structural Damage Localization and Quantification Technique Using Time Domain Vibration Data

The conventional approaches for detecting structural degradation are time-consuming, labor-intensive, and costly. The physical monitoring of the structure also poses risks to the health and safety of supervisors. Therefore, damage estimation of any structure using artificial intelligence (AI), more specifically deep learning (DL), is becoming more significant in civil infrastructure. In the presented research article, an efficient two-stage damage detection method is proposed for structural damage detection (SDD) from time domain vibration signals. The proposed method utilizes two-dimensional convolutional neural network (2D-CNN) architecture as a DL algorithm for damage detection. Here, a computer-aided damage detection method for steel beam and frame-type structures is developed using 2D-CNN algorithm in the Google Colab platform. The effectiveness of the proposed method is first verified, and it provides more than 90% accuracy for identifying the damage location and severity of a cantilever beam for single- and multi-damage scenarios from numerically simulated noisy displacement data. The algorithm is also experimentally validated through the raw acceleration data of damaged steel frame joints collected from the Qatar University Grandstand simulator (QUGS). The proposed 2D-CNN algorithm performs better than other DL algorithms by achieving 100% accuracy within 10 epochs for damage detection of steel frames using QUGS data. It demonstrates significant potential for detecting damage location and quantifying damages for single- and multi-damage scenarios using noise-free and noisy datasets. The primary contribution of this study resides in the implementation of two-stage damage detection algorithm utilizing 2D-CNN with time domain vibration response for multiclass damage identification and quantification.

Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks

This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions.

On the analysis of antiresonance frequencies for structural damage localization

During the service life of long-lasting constructions, structural changes occur that can lead to damage or failure. For reasons of economic and ecological sustainability, it is of great importance to detect these changes as early as possible to ensure safe operation and to be able to initiate maintenance measures if necessary. To detect and localize emerging damages, structural health monitoring systems can be employed. The realization of such systems requires methods that can derive information about the condition of the monitored object from measurement data. To develop a better understanding of how damage affects structural integrity, simulation models of generic structures can be used. In most cases, structural damage can be generalized as some type of local stiffness reduction, causing changes in the dynamic properties of the structure. From a numerical perspective, this means that the dynamic stiffness matrix of the system changes, which can lead to shifts in resonance and antiresonance frequencies. Since resonance frequencies are global characteristics of the structure, they may contain information about the occurrence of structural damage, but not about its location. Therefore, this paper investigates whether and how the observation of the antiresonance frequencies can be used to both detect and localize damages.

Experimental Study on Bridge Structural Damage Identification Based on Quasi-Static Displacement Effects and Wavelet Packet Decomposition

Based on the displacement-induced linearity error curve and the theory of wavelet packet analysis, a bridge structural damage identification method is proposed, integrating two damage indicators: Quasi-Static Displacement-Induced Linearity Error Curve (QSDIL) and Relative Energy Rate of Wavelet Packet Energy Spectrum (RES). This method first constructs the QSDIL damage indicator based on a quasi-static displacement-induced linearity error, which is used for the preliminary localization of the bridge structural damage. Subsequently, relying on the principles of wavelet packet analysis, the method constructs the RES damage indicator for accurate positioning of the damage location in the bridge structure. The proposed method is experimentally validated, and the impact of factors such as single-point damage, multi-point damage, signal noise, lane position, and vehicle weight on the experimental results is investigated. The results indicate that the proposed method exhibits excellent identification performance for the location of structural damage in both single-point and multi-point damage scenarios, with good agreement between experimental and theoretical values. As the signal-to-noise ratio decreases, the accuracy and precision of the RES curve in locating the bridge structural damage position exhibit a nonlinear decreasing trend, with relatively small identification errors observed at noise levels of 90 dB to 100 dB. Different lane positions have a minimal impact on the damage identification effectiveness. With increasing vehicle weight, both QSDIL and RES curves show an increasing trend in peak values, facilitating the localization of bridge structural damage positions.

A damage-informed neural network framework for structural damage identification

In this work, an effective Damage-Informed Neural Network (DINN) is first developed to pinpoint the position and extent of structural damage. Instead of resolving the damage identification problem by conventional numerical methods, a Deep Neural Network (DNN) is employed to minimize the loss function which is designed by combining multiple damage location assurance criterion and flexibility matrices to guide the training process. In our computational framework, the parameters of the network, which include both weights and biases, are treated as new design variables instead of damage ratios. Therein, the training data consists only of a set of spatial coordinates of elements, whilst corresponding the damaged ratios of elements unknown to the network are factored into the output. To achieve the goal, the loss value is calculated relying on the predicted damage ratios with supporting Finite Element Analysis (FEA). Additionally, Bayesian Optimization (BO) algorithm is used to automatically tune hyperparameters of the network for enhancing reliability in damage identification. Several numerical examples for damage localization of truss and frame structures are investigated to evaluate the effectiveness and reliability of the suggested methodology. The obtained results point out that our model not only correctly locates the actual damage sites but also requires the least number of structural analyses and faster convergence rate compared with other algorithms.

Integration of multi-point influence line information for damage localization of bridge structures

Integration of multi-point influence line information for damage localization of bridge structures

Global-local feature cross-fusion network for ultrasonic guided wave-based damage localization in composite structures

Global-local feature cross-fusion network for ultrasonic guided wave-based damage localization in composite structures

A Structural Damage Localization Method Based on Empirical Probability Mass Function of ARMAX Model Residual and Kullback–Leibler Divergence

Damage localization is very significant in engineering applications. The existing method based on the chi-square distribution of an autoregressive moving average with exogenous inputs (ARMAX) model residual is not applicable for these realistic excitations except Gaussian excitation. To solve the above problem, this paper presents a structural damage localization method based on the empirical probability mass function (EPMF) of the ARMAX model residual and Kullback–Leibler (KL) divergence. In detail, we employ empirical data analysis (EDA) approach to estimate the EPMF of the ARMAX model residual of the data generated by the arbitrary excitation because EDA does not need any a priori knowledge about the model residual. Moreover, the KL divergence is introduced to measure the dissimilarity of the EPMFs in undamaged and damaged states to prove that our method is effective for arbitrary excitation. Finally, the semi-parametric extreme value theory is used to estimate the reliable threshold for localizing the damage. Numerical simulated and experimental results illustrate that the proposed method localizes the damage under different excitations, respectively.

The comparison of the FRF curvature technique and FRF mutation coefficient technique on the identification effect of beam structure damage location

Improving the rigidity of machine tools for identifying the weak links of structural stiffness is one of the important issues in mechanical engineering. In this paper, a new weak index is proposed on a cantilever structure of the machine tool to identify the weak stiffness link. The weak index is a frequency parameter obtained via the vibration test method. In this method, the FRF coefficient of variation technique is proposed based on the FRF curvature method. The change of weak index is used to identify the weak link in the structural stiffness. The element stiffness weakening method that employs numerical examples of a cantilever beam and an elastic support beam is used to simulate the weak links of the stiffness. Compared with the FRF curvature ratio (FRF_CR) method, the FRF mutation coefficient is improved in the same frequency range. Moreover, the FRF_MCR indicator has more frequency points than the FRF_CR indicator in the same frequency range.