Damage Identification Research Articles

Energy-based criteria for identification of impact-induced interfacial damage of longitudinally connected slab ballastless track structure

Interfacial damage as a potential threat affects the stability of the longitudinally connected slab ballastless track structure (LCSBTS). In most related research, the interfacial damage of LCSBTS is identified in terms of stiffness degradation, which makes it difficult to explore the role of energy in the development of the interfacial damage. As an alternative, a set of energy-based criteria rooted in a bilinear cohesive zone model was proposed, together with a new damage indicator that evaluates the loss of interfacial fracture energy. The proposed criteria were utilized in the simulation of the interfacial damage of LCSBTS subjected to wheel impact. Results show that, the energy-based damage indicator describes the growth of the interfacial damage at a slower speed than the stiffness-based damage indicator, better coordinating with the variation of the interfacial fracture energy. Under wheel impact, interfacial damages are most likely to occur out of a peanut-shaped region surrounding the impact point where shear stress surpasses tension stress. However, by considering a coupling action of the positive temperature gradient, severe interfacial damages appear around the center of the track slab where tension stress surpasses shear stress and interfacial damages caused by shear stress are located near the longitudinal edges of the track slab.

Comparison of the Applicability of Bayesian Filters for System Identification of Sudden Structural Damage

Comparison of the Applicability of Bayesian Filters for System Identification of Sudden Structural Damage

Enhanced operational modal analysis and change point detection for vibration-based structural health monitoring of bridges

One of the most promising uses of vibration-based structural health monitoring (VBSHM) in bridge damage detection is the tracking of modes through long-term repeated or continuous operational modal analysis (OMA). Any shifts in modal parameters over time can signal structural damage. However, in real-world applications, noise and environmental uncertainties introduce variability in the data, potentially obscuring damage-related changes. To address this, it is essential to establish and understand the temporal trends and behavior of the estimated modal parameters, enabling accurate interpretation of the engineering data. This paper presents a detailed study focusing on data-driven techniques to improve the OMA results by determining the causes of modal variability and establishing modal models to filter out these known causes of variability. It explores the use of data continuously collected over a period of one month in November 2017 on the Confederation Bridge in eastern Canada. Operational modal analysis is conducted to extract modal frequencies and mode shapes, revealing correlations with environmental and operational factors such as wind, temperature and vehicular traffic. A novel approach using the residuals from regression modal models for damage detection is proposed, utilizing a change point detection algorithm. Results indicate the potential to detect shifts in modal frequencies corresponding to damage scenarios, at lower levels than was previously possible, highlighting the feasibility of using enhanced modal features for sensitive damage identification. Overall, the paper contributes to advancing the understanding of variability in vibration-based structural health monitoring and presents a promising practical technique for improving damage detection results using enhanced operational modal estimates in realistic field applications of a real-world structure.

Corrosion-fatigue damage identification in submerged mooring chain links using remote acoustic emission monitoring

This paper investigates the feasibility of detection, localisation, and monitoring of corrosion-fatigue damage in mooring chain links using remote Acoustic Emission (AE) technique in submerged conditions. A large-scale experiment was conducted on a studless R4 chain retrieved after about two decades of operation offshore. Ultrasound signals were continuously measured using fixed and movable arrays of AE transducers placed on perpendicular planes in the water tank enclosing the chain. The AE parameters extracted from the measured signals have been analysed. AE sources were successfully localised on the 3D geometry of the chain links. The results suggest that damage growth can be detected and localised using non-contact underwater AE transducers.

Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: Structural Health Monitoring in the Context of Industry 4.0

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

Damage identification method based on interval regularization theory

In the field of damage identification, traditional regularization methods neglect the impact of uncertainty factors on the selection of regularization parameters, leading to a decrease in the accuracy of damage identification. Therefore, this study proposes a damage identification based on interval truncated singular value decomposition (DI-ITSVD) method that considers the uncertainty in the selection of regularization parameter. This method treats model errors and measurement noise as interval uncertainties, and introduces the quantified uncertainties into the damage identification solutions through uncertainty propagation methods to determine the interval boundary. Uncertainty regularization parameters are selected to balance residuals and solutions using interval and generalized cross-validation methods. The key aspect of the proposed method in this paper is the integration of interval uncertainty propagation with the truncated singular value decomposition method to ensure the accuracy and stability of the damage identification equation solution. A numerical example of a 29-bar planar truss has been performed to test the effectiveness of the proposed method. The superiority of this method is verified by comparing the identification results with other improved truncated singular value decomposition methods. Finally, the practical application effect of the proposed method was also verified through an experimental work.

Structural damage detection and localization via an unsupervised anomaly detection method

This study introduces an unsupervised machine learning framework for damage detection and localization in Structural Health Monitoring (SHM), leveraging dynamic graph convolutional neural networks and Transformer networks. This approach is specifically tailored to overcome the challenge of limited labeled data in SHM, enabling precise analysis and feature synthesis from sensor-derived time series data for accurate damage identification. Incorporating a novel ‘localization score’ enhances the framework’s precision in pinpointing structural damages by integrating data-driven insights with a physics-informed understanding of structural dynamics. Extensive validations on diverse structures, including a benchmark steel structure and a real-world cable-stayed bridge, underscore the framework’s effectiveness in anomaly detection and damage localization, showcasing its potential to safeguard critical infrastructure through advanced data-effective machine learning techniques.

Structural damage identification based on Bayesian updating

Due to factors such as model accuracy, sensor arrangement, noise influence, eigenvalue selection and the influence of local damage on the overall response, the structural damage identification process inevitably has uncertainties, so it is necessary to introduce probabilistic statistical methods to improve its accuracy and robustness. In this paper, structural damage identification is carried out based on Bayesian updating. Bayesian updating is combined with real-time monitoring data (e.g. displacement, acceleration), and the real-time data reflect the current state of the structure and contain damage information. The experimental results show that the update results of the Bayesian method correlate well with the actual data, which verifies the feasibility of Bayesian updating in structural damage identification. The structural prior probability can be effectively updated by this method, which shows high accuracy and robustness in structural damage identification and is able to maintain stable performance under different working conditions and environments. Based on Bayesian updating, this structural damage identification method provides a new technical means for structural health monitoring and damage assessment, with broad application prospect and development space.

An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection

Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams.

Structural Diagnosis of Solid Rocket Motors Using Neural Networks and Embedded Optical Strain Sensors

The main failures that could deteriorate the reliable operation of solid rocket motors (SRMs) and lead to catastrophic events are related to bore cracks and delamination. Current SRMs’ predictive assessment and damage identification practices include time-consuming and cost-demanding destructive inspection techniques. By considering state-of-the-art optical strain sensors based on fiber Bragg gratings, a theoretical study on the use of such sensors embedded in the circumference of the composite propellant grain for damage detection is presented. Deep neural networks were considered for the accurate prediction of the presence and extent of the defects, trained using synthetic datasets derived through finite element analysis method. The evaluation of this combined approach proved highly efficient in discriminating between the healthy and the damaged condition, with an accuracy higher than 98%, and in predicting the extent of the defect with an error of 2.3 mm for the bore crack depth and 1.6° for the delamination angle (for a typical ~406 mm diameter grain) in the worst case of coexistent defects. This work suggests the basis for complete diagnosis of solid rocket motors by overcoming certain integration and performance limitations of currently employed dual bond stress and temperature sensors via the more scalable, safe, sensitive, and robust solution of fiber optic strain sensors.

Damage identification of statically indeterminate trusses based on Bayesian updating

Amidst China's rapid development, most structures and edifices have advanced into the mid-service life phase, where components and frameworks gradually undergo aging and deterioration. Ensuring the safety and functionality of buildings necessitates a heightened emphasis on structural health assessments. This study aims to explore a more cost-effective and time-efficient diagnostic strategy for detecting damage in statically indeterminate trusses, while exploring techniques to uphold data precision by reducing loading nodes and measurement parameters. Employing OpenSees for model construction, this paper utilizes Bayesian updating through Monte Carlo rejection sampling to pinpoint damage in statically indeterminate trusses. Moreover, it unveils the fundamental principles behind optimal loading and measurement strategies. Findings from conducted experiments advocate for a comprehensive approach integrating displacement measurements across multiple axes for enhanced accuracy, surpassing single-direction measurements. Validation of the virtual work principle underscores this method's efficacy. Ultimately, this methodology holds promise for curbing engineering expenses and amplifying operational efficiency in real scenarios.

CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data

This study presents a convolutional neural network (CNN) deep learning approach for identifying damage in submerged structure-foundation systems using vibration data. Firstly, foundation damage in a lab-scale caisson-foundation system is simulated to measure time-history responses. Singular value decomposition (SVD) responses are derived from the time-history responses. Secondly, the 1-D CNN deep learning model is trained using both the time-history responses and SVD responses. Finally, the trained CNN models are implemented to evaluate the foundation damage under conditions of noise contamination and partially untrained data. The experimental results demonstrate the effectiveness of CNN models for damage identification and highlight the comparative strengths of time-history and SVD data. The CNN model trained using SVD data outperforms the other model when under noise contamination conditions, while the CNN model trained using time-history data maintains better accuracy in partially untrained data conditions. Integrating both types of data enhances the accuracy of damage classification.

Review of Vehicle Scanning Method for Bridges from 2004 to 2024

The vehicle scanning method (VSM), originally known as the indirect method, is an efficient method for bridge health monitoring that utilizes mainly the responses collected by the moving test vehicles. This method offers the advantage of mobility, efficiency, and cost-effectiveness as it requires only one or a few vibration sensors mounted on the test vehicle, eliminating the need for deployment of numerous sensors on the bridge. Since its initial proposal by Yang and co-workers in 2004, the VSM has gained intensive attention from researchers worldwide. Over the past two decades, significant progress has been made in various aspects of the VSM, including the identification of bridge frequencies, mode shapes, damping ratios, damages, and surface roughness, as well as its application to railways. Previously, some review papers and the book Vehicle Scanning Method for Bridges were published on the subject. However, research on the subject continues to boom at a speed that cannot be adequately by existing review papers or book, as judged by the fast-increasing number of relevant publications. In order to provide researchers with an overall understanding of the up-to-date researches on the VSM, a state-of-the-art review of the related research conducted worldwide is compiled in this paper. Comments and recommendations will be provided at appropriate points, and concluding remarks, including future research directions, will be presented at the end of the paper.

In situ monitoring of high-temperature creep damage in CrMoV high-strength structural steel using acoustic emission

The accurate detection and evaluation of creep damage in structural steel is essential for ensuring the safety and reliability of high-temperature structures. In this work, the in situ monitoring and identification of high-temperature creep damage in CrMoV high-strength steel under different stress levels was conducted using the acoustic emission (AE) technique. A denoising procedure was proposed and applied to raw AE signals to reduce the noise unrelated to the growth of creep damage. To characterize the creep damage both qualitatively and quantitatively, multiple AE features were extracted from the creep-related signals, including peak amplitude, count, and information entropy. The results showed that a sudden increase in multiple cumulative parameters accompanied by high-entropy and high-count AE signals can accurately identify the transition from the secondary to the tertiary stage and can offer an early warning of the final rupture. In the log-log scale, the quantitative relationship between the AE rate and the minimum creep rate was found to be linear. Additionally, the substantial plastic deformation, and the nucleation, growth and coalescence of creep cavities were the dominating source mechanisms contributing to the generation of numerous high-count and high-entropy AE signals in the tertiary creep stage. Findings from this work will offer an approach for in situ monitoring and evaluation of the state of creep damage of high-temperature structures based on AE.

Damage identification of plain-woven composites at T > Tg using AE: Damage clustering and initiation detection

In this work, the damage modes and their initiation detections of plain-woven composites under tensile and compressive loads at T > Tg are investigated by conducting a comprehensive analysis of AE signals, including the feature selection, algorithm comparison and clustering validity. According to a newly proposed Pearson correlation coefficient cumulative criterion, the PF and PA are the most representative features of AE signals. A systematic comparison of K-Means, GMM and Hierarchical model, reveals that the Hierarchical model is the most suitable clustering algorithm for plain-woven composites at T > Tg. The cluster validity indexes combined with microscopic observations identify matrix cracking, interface debonding and fiber breakage as the three dominant damage modes, with PF ranges of [0–200 kHz], [200–350 kHz] and [350–600 kHz], respectively, and independent of different loading conditions. Three damage initiation criteria, sentry function, b-value and cumulative AE counts, are compared and the sentry function is determined to be optimal. The damage initiations of matrix cracking, interface debonding and fiber breakage within plain-woven composites at T > Tg can be effectively detected by AE.

Application of Artificial Neural Networks to a Model of a Helicopter Rotor Blade for Damage Identification in Realistic Load Conditions.

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load's variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS.

Piezoelectric Impedance-based Structural Damage Identification Empowered with Tunable Circuitry Integration and Multi-objective Optimization based Inverse Analysis

Abstract The piezoelectric impedance-based technique is increasingly recognized for its promise in structural health monitoring and damage identification. Built upon their self-sensing actuation capability, piezoelectric transducers can be integrated to host structures to acquire the system-level impedance information in high frequency range with small wavelength. Furthermore, the frequency-sweeping harmonic excitations in impedance measurements lead to the potential for model-based inverse identification of damage location and severity. A major challenge in damage identification, however, is that the inverse analysis is generally underdetermined, as the measurement information may not be adequate to yield a unique solution. In this research, a new methodology of tunable sensing in conjunction with multi-objective optimization inverse analysis is established. Taking advantage of the two-way electromechanical coupling of piezoelectric transducers, tunable inductance is integrated into the measurement circuit. For the same damage scenario, by tuning the inductance to a series of values, a family of impedance measurements can be acquired. Meanwhile, the inverse analysis is cast into a multi-objective optimization problem, aiming at minimizing the difference between measurement and model prediction and achieving sparsity in damage index vector. A Q-learning based multi-objective particle swarm optimization is synthesized to reach a small yet diverse solution set. We report the circuitry integration details as well as the algorithm enhancement with systematic case investigations. It is validated that the new methodology with enriched measurement can produce smaller solution set encompassing the true damage scenario, thereby providing vital information for diagnoses and prognosis.

Relation of ICAM-1 and VCAM-1 markers in COVID-19 patients in Kirkuk province.

The advent of the Coronavirus Disease 2019 (COVID-19) has presented a substantial and urgent global public health issue. Biomarkers have the potential to be utilized for the identification of endothelium and/or alveolar epithelial damage in instances of COVID-19 infection. to evaluate the levels of Intercellular adhesion molecule-1 (ICAM-1) and Vascular cell adhesion molecule (VCAM-1) biomarkers in hospitalized patients who tested positive for COVID-19 infection using Polymerase Chain Reaction (PCR) with the virus specific Immunoglobulins; IgM, and IgG testing. This can help with improved clinical management and treatment programs. A case-control study that involved 90 hospitalized patients who tested positive for COVID-19 and 40 apparently healthy control patients, subjects in both groups underwent nasopharyngeal swabs for PCR and blood sample collection for evaluation of serum; IgM, IgG, ICAM-1 and VCAM-1 levels. Males made up the vast majority of the patients (78.9%), with only a minor percentage of females (21.1%) P value 0.1641. Furthermore, every patient in this study had a minimum of one risk factor for COVID-19. The investigator's results show that COVID-19 patients had higher amounts of endothelial cell adhesion indicators (ICAM-1 and VCAM-1) with mean values of 126.27 ± 89.51 ng/mL and 109.74 ± 96.57 ng/mL respectively. While, ICAM-1 and VCAM-1, were present at normal levels in the control group with difference P value 0.0028 and 0.0032 in comparison to the patient's group respectively. The adhesive markers ICAM and VCAM play a crucial role in the development of COVID-19 and the strong endothelial activation and dysfunction linked to both acute and persistent immunological responses is shown by the substantial correlation found in COVID-19 patients between the presence of IgM and IgG antibodies and higher levels of ICAM-1 and VCAM-1.

Wearable damaged clothing fabric image recognition system based on image restoration algorithm

With the improvement of people's living standards and the rapid growth of national technological strength, wearable devices have gradually integrated into people's daily lives, making them convenient for people's work and lives. Traditional fabric detection methods often rely on manual visual inspection, which has low efficiency and accuracy that does not meet the requirements. In view of this, the research has innovatively integrated GAN, CNN, field Programmable gate array (FPGA), multi-stage image recovery algorithm based on graph neural network, for high frequency and low frequency fusion, and combined it with wearable devices to develop a new wearable fabric recovery and recognition system. The results showed that in the COCO dataset, after 250 iterations, the WFR2S algorithm achieved a damage identification accuracy of 95.5 %, a mean absolute error of 0.011, a real-time processing speed of 30 fps, and a resource consumption of 2.4 W. These data demonstrate the significant performance advantages of WFR2S in image recovery and damage detection. The newly developed image recovery algorithm improves the accuracy, availability and stability of damage recognition in garment fabrics. This study not only has theoretical value but also has broad application prospects in practical application. The aim is to contribute to the intelligent development of the garment industry.

Identification of hanger damage based on deflection influence matrix

Tied-arch bridges, a vital component of modern infrastructure, are susceptible to various forms of damage, particularly hangers. The detection and identification of such damages are crucial for maintaining structural integrity and safety. However, traditional methods face challenges in terms of accuracy and efficiency. This study aims to develop a refined method for hanger damage identification in tied-arch bridges, to address the limitations of existing techniques. By focusing on deflection changes at the anchoring points between the hangers and tie beams, we sought to enhance the precision of damage detection. We propose an innovative approach based on the concept of influence lines, introducing the ‘generalized deflection difference influence line'and the ‘deflection difference influence matrix’. Then proposed a new identification index for identifying the damaged hanger after matrix. An actual tied-arch bridge was used to validate the proposed approach. A detailed three-dimensional finite element model of the bridge was developed and calibrated using dynamic and static response data. Thirty different hanger-damage conditions were simulated to evaluate the effectiveness of the proposed method. Our findings reveal that the deflection difference influence matrix offers more detailed and comprehensive information on bridge distribution points than traditional methods. Our method proved effective in identifying hanger damage, irrespective of its location on the bridge. In additionally, the identification efficiency of the method can be improved by adjusting the magnitude of the applied load, with larger loads amplifying the detectability of damage. This study highlights the potential of the deflection difference influence matrix to revolutionize hanger damage identification for tied-arch bridges. Its adaptability, accuracy, and efficiency are significant advancements over existing methods. This study successfully demonstrates an innovative and reliable method for hanger damage identification.