Damage Detection Research Articles

Integrating electro-mechanical impedance data with machine learning for damage detection and classification of blended concrete systems

In recent years, the assessment of structural integrity and durability of concrete systems has significantly advanced with the integration of novel sensing technologies and data-driven techniques. This study introduces an innovative approach that utilizes electro-mechanical impedance (EMI) data acquired through embedded piezo sensors (EPS) to monitor and analyze the condition of blended concrete systems. Moreover, the study involves blended concrete specimens are subjected to controlled damage scenarios under mechanical loadings to establish EPS as an early warning sensor for detecting damage in these systems. Furthermore, the extent of damage under these loadings is comprehensively evaluated using statistical indices and an equivalent stiffness parameter. Additionally, the study focuses on the application of machine learning (ML) algorithms to interpret EMI data for efficient damage classification. ML models, such as decision trees (DT), random forests (RF), k-nearest neighbors (KNN), support vector machines (SVM), and naive Bayes (NB) are trained to identify patterns correlating with different types and extents of structural damage. Among them, the RF classifier exhibits the highest accuracy of 0.91, followed by DT, KNN, SVM, and NB with accuracies of 0.88, 0.86, 0.68, and 0.17 respectively. The integration of EMI data with ML approaches demonstrates significant potential to revolutionize predictive maintenance strategies, enabling precise classification of structural damage and providing critical insights for engineers and stakeholders to proactively address issues, thereby significantly enhancing the safety, durability, and lifespan of concrete infrastructure. This study not only extends the current understanding of EMI data applications but also opens new paths for automated and real-time structural health monitoring.

Dynamics of Gill Responses to a Natural Infection with Neoparamoeba perurans in Farmed Tasmanian Atlantic Salmon.

Gill health has become a significant global challenge for Atlantic salmon (Salmo salar) aquaculture, particularly during the marine phase of farming. The increasing prevalence of gill pathologies has been linked to rising seawater temperatures, underscoring the need to evaluate existing tools for monitoring gill health and to develop novel approaches for early detection. In this study, we investigated the gill responses of commercially farmed Atlantic salmon to natural infection with Neoparamoeba perurans during an outbreak of amoebic gill disease (AGD) in Tasmania. Our focus spanned the low AGD prevalence, high AGD prevalence, and post-freshwater treatment stages of the outbreak. Evaluations of gill tissue included assessments of the gross AGD score, histopathological score, abundance of N. perurans (measured by 18S rRNA gene expression), and expression levels of inflammation-related transcripts. We demonstrated a strong correlation between different measures of AGD-related gill pathology and significant differences between distinct stages of the N. perurans outbreak. Post-treatment, fish exhibited considerable variability in their responses to the freshwater bath, highlighting the necessity for personalized management strategies that consider genetic, environmental, and health status factors. The expression patterns of angiogenin-1 (ANG1) and complement C1q tumour necrosis factor-related protein 3-like (C1QTNF3) emphasize their potential as biomarkers for early detection of gill damage in salmon aquaculture worldwide.

Detection of ballastless track interlayer gap based on vehicle’s multivariate dynamic response and deep learning

To adapt to the rapid detection of interlayer damage in ballastless track structures of high-speed railways, a cement asphalt mortar (CAM) gap localization and damage degree classification scheme based on multivariate data fusion and deep learning is proposed. Based on vertical axle box acceleration (VABA) and vertical wheel-rail force (VWRF) data, the variation patterns of multi-dynamic response data of vehicles under the edge type and internal type interlayer gaps are analyzed. An improved ensemble local mean decomposition algorithm (IELMD) is designed to achieve data denoising and preliminary enhancement preprocessing of weak damage features for VABA and VWRF under interlayer gaps. The measured and simulated VABA and VWRF constituted multiple dynamic response data sets for interlayer gap detection. An interlayer gap detection model: DTA-Tcnformer is constructed, integrating a dual temporal convolutional network with an attention mechanism, transformer architecture, and gate control mechanism. Multiple dynamic response data are input into the model to locate and classify 24 cases of the two interlayer gap types. The influence of damage cases and vehicle speeds on the location effect is analyzed. The learning ability of the proposed model on the gap features is visualized. The misidentification and omission of the gap cases are calculated, and the comprehensive recognition accuracy is 92.48 %. The recognition performance of the proposed model is compared and verified.

Hybrid multi-modal NDE sensing system for in-motion detection and localization of rolling contact fatigue damage in rails

This article presents a multi-modal hybrid-probe approach to nondestructive inspection of RCF cracks and damage in rails. A combination of electromagnetic (EM) and ultrasonic testing (UT) techniques are presented, which allows for complementary physics to be utilized to enhance detection and characterization of surface and sub-surface cracks in a non-contact manner at high speeds. A novel integrated design which combines the motion-induced eddy current (MIEC) effect and ultrasonic Rayleigh surface waves generated and detected using electromagnetic acoustic transducer (EMAT) is presented. The hybrid probe was tested at low speeds to demonstrate an increased damage localization capability. This was carried out using a data registration and fusion approach between the sensing modalities. Finally, the capability of MIEC effect at high-speeds is demonstrated. The results show that the hybrid probe has a high potential for in-motion, high-speed damage detection and characterization in the future.

Meta-model structural monitoring with cutting-edge AAE-VMD fusion alongside optimized machine learning methods

Transfer learning significantly enhances machine learning by leveraging knowledge from one dataset to boost performance on another, which is particularly beneficial when labelled data ares limited or costly. Sensor fusion, an essential technique in structural health monitoring, improves the effectiveness of transfer learning by combining data from multiple sensors to extract more informative features. This article proposes a novel meta-model structural monitoring using a new data fusion method named the adversarial autoencoder (AAE)–variational mode decomposition (VMD) algorithm, which integrates optimization algorithms, transfer learning, and machine learning techniques for damage detection tasks. The proposed meta-model combines transfer learning with an optimized machine learning framework for training and employs pretrained models for testing across diverse datasets. First, the tensors are fused using the AAE approach into 300 data points, reducing noise and outliers, performing dimensionality reduction, and enriching the training and test datasets. Then, a preprocessing step involves decomposing and denoising tensors using the VMD algorithm, followed by selecting the most informative intrinsic mode functions (IMFs) based on their variance. These selected IMFs are concatenated, and 13 statistical features are extracted from them and used as inputs for machine learning models. Various optimization algorithms are employed to fine-tune the hyperparameters of the machine learning models for optimal classification results. Validation of this meta-model utilizes the dataset collected from a steel grandstand structure, available in benchmark dataset format. For decomposed fused tensors, both particle swarm optimization and covariance matrix adaptation evolution strategy emerge as equally effective optimization techniques for K-nearest neighbours (KNN), consistently achieving the highest mean test accuracy and F1 score of 99.5%. Conversely, KNN stands out as a robust and reliable classification algorithm for denoised fused tensors in transfer learning classification problems, consistently demonstrating perfect accuracy and an F1 score of 1.0 ± 0.0 across all optimization techniques.

A Review of Non-Destructive Testing for Lithium Batteries

With the rapid development of mobile devices, electronic products, and electric vehicles, lithium batteries have shown great potential for energy storage, attributed to their long endurance and high energy density. In order to ensure the safety of lithium batteries, it is essential to monitor the state of health and state of charge/discharge. There are commonly two methods for measuring lithium batteries: destructive testing and non-destructive testing. Destructive testing is not suitable for in situ or non-destructive analysis as it can cause irreversible deformation or damage to the battery. Herein, this review focuses on three non-destructive testing methods for lithium batteries, including ultrasonic testing, computer tomography, and nuclear magnetic resonance. Ultrasonic testing is widely used in crack and fatigue damage detection. X-ray computer tomography and neutron tomography have gained increasing attention in monitoring the health status of lithium batteries. Nuclear magnetic resonance can be used to conduct in situ and ex situ detection. In this review, non-destructive testing of lithium batteries is summarized, including the current status, achievements, and perspectives of this technology.

Panel miRNAs are potential diagnostic markers for chronic kidney diseases: a systematic review and meta-analysis

BackgroundAccurate detection of kidney damage is key to preventing renal failure, and identifying biomarkers is essential for this purpose. We aimed to assess the accuracy of miRNAs as diagnostic tools for chronic kidney disease (CKD).MethodsWe thoroughly searched five databases (MEDLINE, Web of Science, Embase, Scopus, and CENTRAL) and performed a meta-analysis using R software. We assessed the overall diagnostic potential using the pooled area under the curve (pAUC), sensitivity (SEN), and specificity (SPE) values and the risk of bias by using the QUADAS-2 tool. The study protocol was registered on PROSPERO (CRD42021282785).ResultsWe analyzed data from 8351 CKD patients, 2989 healthy individuals, and 4331 people with chronic diseases. Among the single miRNAs, the pooled SEN was 0.82, and the SPE was 0.81 for diabetic nephropathy (DN) vs. diabetes mellitus (DM). The SEN and SPE were 0.91 and 0.89 for DN and healthy controls, respectively. miR-192 was the most frequently reported miRNA in DN patients, with a pAUC of 0.91 and SEN and SPE of 0.89 and 0.89, respectively, compared to those in healthy controls. The panel of miRNAs outperformed the single miRNAs (pAUC of 0.86 vs. 0.79, p < 0.05). The SEN and SPE of the panel miRNAs were 0.89 and 0.73, respectively, for DN vs. DM. In the lupus nephritis (LN) vs. systemic lupus erythematosus (SLE) cohorts, the SEN and SPE were 0.84 and 0.81, respectively. Urinary miRNAs tended to be more effective than blood miRNAs (p = 0.06).ConclusionMiRNAs show promise as effective diagnostic markers for CKD. The detection of miRNAs in urine and the use of a panel of miRNAs allows more accurate diagnosis.

Enhancing geotechnical damage detection with deep learning: a convolutional neural network approach.

Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes 'damage' and 'intact' is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.

A review on vision-based deep learning techniques for damage detection in bolted joints

A review on vision-based deep learning techniques for damage detection in bolted joints

Serum fibulin-3 as a diagnostic and prognostic biomarker in patients with knee osteoarthritis.

Osteoarthritis is a chronic degenerative disorder with rising prevalence. Early detection of structural damage is difficult. Consequently, there is a pressing demand for reliable biomarkers that enable an earlier diagnosis of osteoarthritis. The aim is to investigate the level of serum fibulin-3 in patients with primary knee osteoarthritis and its correlation with disease severity. A case-control study was conducted at the Baqubah Teaching Hospital from November 2023 to January 2024. One hundred twenty persons participated in this study (eighty females with the diagnosis of knee osteoarthritis in its early and late stages, and forty age-matched, apparently healthy control. Serum fibulin-3, ESR, CRP, and calcium levels were measured for all participants. Ethical approval was obtained. SPSS was used for data analysis. Patients with osteoarthritis had considerably higher serum levels of fibulin-3. In patients with late-stage knee osteoarthritis, this rise was greater than in earlier stages. Serum fibulin and ESR are positively correlated. Fibulin's area under the curve is 0.830 for diagnosis and 0.709 for differentiating between osteoarthritis's early and late stages. Serum levels of fibulin-3 can act as diagnostic markers for OA and may be useful in determining the severity of knee osteoarthritis.

Automated ultrasonic scattering energy method for evaluation of small-sized cracking damage in concrete

The aim of this study was to develop an automatic ultrasonic scattering energy method (USEM) to evaluate small-scale damage to concrete. The automated system measured Leaky Rayleigh waves from the specimens. Herein, the energy of the scattering portion of waves caused by cracks was extracted using a 2D Fourier transform. USEM has the advantage of detecting cracking damage smaller than the applied ultrasonic wavelength, which is problematic when conventional ultrasonic methods are applied to concrete. The proposed methodology was evaluated using laboratory-scale specimens and a concrete bridge exposed to freeze–thaw cycles in the field. The results of the extracted scattering energy demonstrated that the USEM is sufficiently sensitive to detect and localize subwavelength damages of 0.05 mm in concrete. Furthermore, the proposed USEM was compared with conventional ultrasonic pulse velocity with respect to the accuracy of damage detection.

Semantic segmentation for plant leaf disease classification and damage detection: A deep learning approach

Agriculture sustains the livelihoods of a significant portion of India's rural population, yet challenges persist in manual practices and disease management. To address these issues, this paper presents an automated plant leaf damage detection and disease identification system leveraging advanced deep learning techniques. The proposed method consists of six stages: first, utilizing YOLOv8 for region of interest identification from drone images; second, employing DeepLabV3+ for background removal and facilitating disease classification; third, implementing a CNN model for accurate disease classification achieving high training and validation accuracies (96.97 % and 92.89 %, respectively); fourth, utilizing UNet semantic segmentation for precise damage detection at a pixel level with an evaluation accuracy of 99 %; fifth, evaluating disease severity; and sixth, suggesting tailored remedies based on disease type and damage state. Experimental analysis using the Plant Village dataset demonstrates the effectiveness of the proposed method in detecting various defects in plants such as apple, tomato, and corn. This automated approach holds promise for enhancing agricultural productivity and disease management in India and beyond.

Intensity Analysis Method of Acoustic Emission Signals for the Damage Monitoring of Post-Tensioned Concrete Beams.

Acoustic emission (AE) is well suited for the real-time monitoring and detection of damage in reinforced concrete structures. In this study, loading/unloading cycles up to failure were applied on three different full-scale beams, each with varying defect morphologies. An intensity analysis method was employed to assess the damage sensitivities of the defective structures under stress conditions. Specifically, the calm ratio, load ratio, severity, and historical index were identified as statistical parameters that can provide global information on the damage level. Consequently, they can be easily used as damage evolution indexes for reinforced concrete structures. Correlations between these parameters were investigated to better discriminate between their potentials and identify critical levels that might not be evident using parametric analysis. The AEI chart helps locate damaging areas, aiding focused repairs. For defected beams with broken strands, at low load, HI and SI fall in zone B (damage detected). At cycles 4 and 6, with significant deflection, they fall into a critical zone, E (severe damage). Comparing post-tensioned beams revealed defects correlating with damage susceptibility. B3 beams with diffused defects displayed high activity at higher loads. Applying a load-calm ratio chart, initial minor damage worsened progressively. Severe damage was prominent in defective B2 and B3 beams, reaching zone 3. The variation in the acquired parameters over time can then be considered as an affordable and reliable indicator of damage progression.

Image acquisition technology for unmanned aerial vehicles based on YOLO - Illustrated by the case of wind turbine blade inspection

Wind energy, as a renewable energy source, is becoming increasingly important. The maintenance and damage detection of wind turbine blades are particularly crucial. For this purpose, the study aims to optimize the You Only Look Once (YOLO) processing algorithm for drone images to improve the detection efficiency. Firstly, the damage images captured by drones are preprocessed and optimized, including deblurring, noise reduction, and image enhancement. Subsequently, the YOLOv5 model is improved in terms of structure and regression function, and a novel damage detection model is proposed. The research results indicated that the minimum loss function value of the improved model was 2.75, the average accuracy was 95 %, and the highest intersection over union was 91 %. After simulation testing, the detection effect of this model on abrasion, crackle, edge cracking, and coating peeling images was significantly better than other models in the same series. Its average time was as short as 2.43 s, reaching a maximum frame rate of 35.46. From this, the combination of drone image technology and improved image processing algorithm has a positive impact on improving the operational efficiency and safety of wind turbine blades. Compared with the traditional methods, the proposed model has significant advantages in terms of accuracy and real-time performance of damage detection, providing a new technical means for efficient maintenance of wind turbines. Meanwhile, the method shows high robustness and reliability in different types of damage detection, demonstrating the extensive potential in practical applications.

Damage detection of beam-like structures using a combination of wavelet transform and subtraction of intact and damaged mode shapes

Detection of damages with low levels has been one of the most critical challenges. As a result, many damage detection methods cannot detect damages or cracks with a level lower than 10%. On-surface damages as low-level damages are challenging to localize. A new technique is proposed to eliminate this challenge based on wavelet transformation of the difference in damaged and intact mode shapes. In this way, a finite element model is developed for obtaining governing equations of thin beams. The developed finite element model provides the mode shape signals. Then, the signals are decomposed by wavelet transform. The findings of this study show that in both numerical and experimental investigations, the proposed method is very efficient since the proposed method can detect on-surface damages having a level below 10%.

Vibration-based damage detection of beams using Hybrid Genetic Algorithm with combined l0 and l1 regularization

Vibration-based damage detection is an effective way of identifying beam damages before they become severe and potentially catastrophic. This paper introduces a novel and robust method for detecting multiple cracks in beams using a Hybrid Genetic Algorithm (HGA). The objective function of the proposed method, based on dynamic properties, can be employed with partial modal information and is enhanced by including the initial residuals between numerical and experimental models along with combined l0 and l1 regularization. This enhancement mitigates the impact of limited modal data, experimental noise, and modeling inaccuracies. Additionally, a method of selecting an appropriate regularization parameter by analyzing residual and solution norm curves is presented. The performance of the proposed method is evaluated and verified using both numerical simulations and experimental studies on a steel cantilever beam under four damage scenarios. In the experimental study, natural frequencies and mode shapes were determined under ambient vibration conditions using Enhanced Frequency Domain Decomposition (EFDD) and the Stochastic Subspace Identification (SSI) method. Finite element models of the beam were developed in ABAQUS software for numerical analysis, and a comparative study of numerical and experimental data was conducted. The proposed approach successfully identifies, locates and quantifies single and multiple damages in the beam, demonstrating its reliability and effectiveness, particularly in scenarios with limited modal data and significant experimental noise.

Assessing the Predictive Accuracy of the Eaton-Littler Classification in Thumb Carpometacarpal Osteoarthritis: A Comparative Analysis with the Outerbridge Classification in a Cohort of 51 Cases.

(1) Background: The objective of this study is to evaluate the predictive value of the Eaton-Littler radiologic classification for thumb carpometacarpal osteoarthritis (CMC OA) relating to intra-articular cartilage damage assessed by the Outerbridge arthroscopic classification. (2) Methods: A total of 51 thumb CMC OA arthroscopies were performed on patients classified as Eaton stages 1, 2, or 3. Post-arthroscopic evaluations of cartilage damage were categorized using the Outerbridge classification. Comparative analyses were conducted between the radiological Eaton stages and the arthroscopic Outerbridge stages. (3) Results: Arthroscopic examination revealed Outerbridge stage 3 and 4 cartilage damage in 26 cases classified as Eaton stage 2 and in 18 cases classified as Eaton stage 3. The detection of severe cartilage damage in patients classified as Eaton stage 2 was unexpected. (4) Conclusions: Arthroscopy demonstrated that many patients with mild radiological degenerative signs exhibited significant cartilage destruction. Although the Eaton classification is widely used for staging thumb CMC OA, it may not accurately reflect the severity of intra-articular damage. The Eaton classification does not reliably predict intra-articular damage in Eaton stage 2 cases.

Streaming variational inference-empowered Bayesian nonparametric clustering for online structural damage detection with transmissibility function

Transmissibility function (TF) is widely applied in damage detection due to its sensitivity to damage and robustness to external excitations, but its application in online damage detection is rarely reported due to challenges in handling data streams. This study proposes a new TF-based online damage detection method that integrates a truncation-free variational inference-based full Dirichlet process Gaussian mixture model (VI-FDPGMM) within a streaming variational inference (SVI) paradigm. As an improved Bayesian nonparametric approach, the truncation-free VI-FDPGMM addresses the issue of truncating mixing components in traditional VI-DPGMM for online learning with increasing data by strategically setting the variational distributions of parameters for the components without assigned data (i.e., inactivated components) to their prior distributions based on the Bayesian viewpoint, which enables computing the probabilities to assign data points to these components and determining the creation of new components. As a result, the truncation-free VI-FDPGMM allows dynamically adding components to the mixture model, providing the flexibility to automatically adapt the number of components for arbitrary amounts of data. This characteristic enables its intuitive integration into the SVI paradigm featured as the variational posterior conditioned on the previous data as the prior when new data are observed, facilitating continuous refinement of the mixture model without repeatedly making inference of previous data. Therefore, the proposed method is highly efficient and well-suited for online damage detection. The proposed method is validated using two case studies, demonstrating its capability to dynamically generate new clusters as new data are available online to indicate the emergence of new damage patterns during the monitoring process, which enables it to perform structural anomaly detection tasks in a semi-supervised manner. Furthermore, the method outperforms some state-of-the-art methods due to its capability for continuous model refinement and robustness in interpreting and capturing uncertainties.

B-Spline signature responses in structural change detection: method development

Damage detection is perhaps the most critical objective of structural health monitoring. Proper documentation of the progression of structural damage from onset to failure enables performance-based maintenance that mitigates risk and contains socioeconomic impact. Recently, damage detection methods that focus on change detection through analyses of the structural global response in the frequency or time domain have garnered attention due to their efficiency and accuracy. This work proposes a novel time-domain, nonparametric, data-driven change detection method. The time-domain B-Spline transfer function is defined as a signature time history response of the structure, referred to in this work as the B-Spline signature response (BSR). BSR may be computed directly through a physics-based simulation, or it be extracted in a discrete form from measurements of displacement, velocity, or acceleration time histories of structural response to any external excitation. The BSR is independent of loading, is a characteristic of the system, and captures the condition of the structure at the time of acquisition without the need for identifying structural properties. In this work, the fundamentals of the BSR concept are introduced first along with the process of extracting a BSR from structural responses to arbitrary excitations. Verification and validation studies are conducted based on computer simulations and laboratory testing and are discussed in detail. Subsequently, an approach is proposed to detect change through correlation of BSR signals acquired at different times during the monitoring period. Any changes in the intrinsic characteristics affecting the response cause BSR changes and correlation loss. The change detection methodology is demonstrated through computer simulations and experimental investigations. Initial parametric studies are conducted to investigate the suitability of the proposed approach to detect small structural changes. This article demonstrates the validity of the proposed method and establishes the framework for further development and case-specific applications.

Damage detection of a cable-stayed footbridge using multiple damage modelling and neural networks

This paper presents a routine for damage detection based on multiple damage modelling and artificial neural networks. The routine aims to identify the existence, location, and the most damaged elements. The first step is to define "critical" regions based on stresses and deformations of the updated numerical model of the structure, as well as the probable order of occurrence in the case of multiple damage by simulating different scenarios. The second step is to train a neural network using the natural frequencies of the intact and damaged numerical models as input data and vectors that indicate the position and magnitude of the damage as expected output. The feed-forward backpropagation neural network was adopted. The approach was used to evaluate a cable-stayed footbridge. A new dynamic test was performed and a vector composed of the identified frequencies was input into the network, which indicated the existence of possible damage. The provided damage scenario was applied to the updated model, along with the prestressing forces of the stays obtained experimentally. The maximum difference between the frequencies of the updated and damaged numerical model with the damage vector provided by the network and the frequencies experimentally identified in the new test was 1.80%.