Damage Detection Analysis Research Articles

Damage detection analysis of 3D braided carbon fiber composites with electro-mechanical behavior

The real-time detection of damage is critical for ensuring the safe application of 3D braided carbon fiber composites (3DBCFC) in both aviation and civilian sectors. In this study, we conducted an electro-mechanical behavior analysis of 3DBCFC using data processing tools to quantify various types of damage. The electro-mechanical behavior data were experimentally measured under tensile conditions. The damage detection capability of different electrical current injection methods was also experimentally validated. A mesoscale finite element model was utilized to investigate the damage mechanism of 3DBCFC under tension. Data processing tools, such as principal component analysis (PCA) and k-means clustering (k-MC), were employed to quantify the different types of damage. The study revealed that oblique current injection provided more comprehensive electrical information, making it more effective for damage detection. The electrical signals obtained through oblique current injection could be processed using data tools to quantify damage in 3DBCFC.

Noise robust damage detection of laminated composites using multichannel wavelet-enhanced deep learning model

This paper presents a noise-robust damage detection framework for composite structures via a commonly used vibration-based non-destructive testing (NDT) method. Recently, deep learning-based models have shown promising performance in the autonomous damage detection of laminated composites; however, the poor noise robustness of these models has plagued data-driven damage detection. Moreover, none of the existing studies on damage detection in laminated composites focus on noise-robust deep learning models with high generalization ability. Therefore, this study proposes a hybrid deep learning framework called a multi-channel convolutional autoencoder-support vector machine (MC-CAE-SVM) based on empirical mode decomposition (EMD) and correlation analysis for noise-robust damage detection. This framework aims to first decompose the vibrational signal from multiple health states into intrinsic mode functions (IMFs). Secondly, highly correlated IMFs were extracted using correlation analysis to remove noisy IMFs. Finally, these IMFs were transformed into a time-frequency representation using continuous wavelet transform (CWT) and input to the MC-CAE-SVM model for feature learning and damage detection. Additionally, the accuracy and sensitivity of the model to damage are enhanced by optimizing the MC-CAE-SVM model hyperparameters. Moreover, anti-noise analysis is performed to check the noise-robustness of the proposed model by incorporating noise at various levels. The results showed that the proposed model can provide better damage detection performance compared to conventional models with excellent noise robustness.

Enhancing acoustic emission analysis for corrosion damage detection in reinforced concrete by k-nearest neighbors method

The Acoustic Emission (AE) method is considered a significant tool for detecting damage in reinforced concrete structures caused by reinforcement corrosion. However, processing a large amount of AE data using traditional analysis methods is time-consuming. Therefore, integrating machine learning (ML) techniques into this process offers a more logical and effective solution to expedite data analysis and enhance damage detection accuracy. This study aims to train an ML model for detecting corrosion damage in reinforced concrete structures using an AE dataset. In pursuit of this goal, it seeks to enhance source localization that contributes to identifying crack patterns through signal-based analysis methods. AE data obtained from a reinforced concrete structural element subjected to accelerated corrosion testing in laboratory conditions have undergone detailed analyses with the ML model. In this context, an ML model capable of learning signals with various wave characteristics and parameters has been successfully integrated into the source localization algorithm, significantly improving AE source localization outcomes. Furthermore, this method is critical for structural safety as it enables the early detection of reinforcement corrosion and precise localization of cracks. The integration of this machine learning model with AE data serves as a valuable tool developed for more effective monitoring of damage in structures.

PECT Composite Defect Detection Algorithm Based on DualGAN

To address the problems of insufficient accuracy and slow reconstruction speed of Planar Electrical Capacitance Tomography (PECT) detection of damaged specimens, a Dual Generative Adversarial Networks (DualGAN)-based PECT image defect detection method is proposed in this paper. The improved particle swarm algorithm with adaptive particle number and L2-norm is used to optimize the sensitivity field, combined with the parallel Landweber algorithm to solve the PECT inverse problem to obtain the dielectric constant distribution map. In the DualGAN network, the Unet generator utilizes an Adam-based local attention mechanism to adjust module weights, facilitating feature extraction and the generation of high-quality transformation images of the Landweber dielectric constant distribution. A PatchGAN discriminator is employed to distinguish between transformation images and real images, using the generated transformation images as target images. Experimental results demonstrate that the sensitivity field, enhanced by the improved particle swarm algorithm and L2-norm normalization, achieves better balance. Furthermore, the addition of a network transformation using the Adam-based local attention weight mechanism on the DualGAN network reduces artifacts in the reconstructed images, resulting in more accurate PECT reconstructions. The PECT image defect detection method, integrating DualGAN, an improved particle swarm optimization algorithm, and a local attention mechanism, has made significant strides in addressing challenges related to image reconstruction accuracy and speed. This technological advancement has enhanced the precision and efficiency of defect detection in carbon fiber composite materials, thereby fostering the broader utilization of planar capacitance tomography technology in industrial damage detection and material defect analysis.

An experimental study on dynamic response of cement concrete pavement under vehicle load using IoT MEMS acceleration sensor network

Understanding the dynamic response of pavements is crucial for comprehending pavement deformation and interaction mechanisms under vehicle load. There is a notable absence of monitoring systems that involve the in-situ embedding of acceleration sensors within full-scale cement concrete pavement slabs. This research introduces an IoT system designed for the monitoring of pavement vibration responses, with its deployment and installation executed within a field engineering project. By collecting and analyzing the pavement vibration responses to vehicle loads, the study investigates the time–frequency characteristics of vibration signals across various vehicle speeds, sensor depths, and lateral distances. The findings reveal that both the amplitude and main frequency of the vibration signal escalate with vehicle speed, whereas the embedding depth of the sensor impacts only the amplitude, leaving the main frequency unaffected. Through statistical analysis, it was determined that effective sensor spacing on cement concrete pavements should not exceed 120 cm to avoid significant signal attenuation, which on average reaches 50 % at a sensor spacing of 90 cm. These insights offer critical guidance for optimizing sensor layouts on cement concrete pavements and lay the foundation for future research on vibration-based damage detection and vehicle information analysis.

Comparative Analysis between Singular Spectral Analysis and Empirical Mode Decomposition for Structural Damage Detection

Context: In recent years, thanks to technological advances in instrumentation and digital signal processing, noninvasive methods to detect structural damage have become increasingly important. Vibration-based structural health monitoring (SHM) techniques allow detecting the presence and location of damage from permanent changes in the fundamental frequencies of signals. A successfully employed method for damage detection is empirical mode decomposition (EMD). Another method, less used in this field of study, is singular spectral analysis (SSA). This paper describes both methods and presents a simulation study aimed at comparing them and identifying which one is more effective in detecting structural damage. Method: The methods of a reference study known as benchmark SHM were applied to facilitate the comparison. To evaluate the effectiveness of both methods, Monte Carlo simulation was employed. To control the random noise and other factors inherent to the simulation, the procedure was repeated 1.000 times for each type of damage. Results: In the case of severe damage, both methods showed a good performance. However, when the damage was slight, the changes in the fundamental frequency were not apparent. However, a significant change in the amplitude level was observed. In this case, SSA obtained the best results. Conclusions: The EMD and SSA methods, together with high-pass filtering, detected severe damage when the acceleration records had low or no noise. When the acceleration records were contaminated with noise, the likelihood of EMD detecting the damage decreased dramatically. One of the advantages of SSA over EMD is that, for moderate or mild damage patterns, the former does not require filters or the use of the Hilbert-Huang transform to detect damage. In general, it was found that SSA was more effective in detecting damage.

Advanced Eddy Current Testing of Carbon Composites

Carbon composites due to their specific properties find applications in various industries, especially in aerospace industry. Widely used carbon fibre reinforced polymers (CFRP) have already been applied even for the aircraft primary structures. The development of advanced diagnostic techniques that are able to easily detect and identify the degradation of the carbon fibre material is still a challenge for various NDT methods. This paper describes the possibility to apply eddy current (EC) for testing of carbon composite structures. Two types of eddy current probes were developed and tested with excellent results. The new conventional eddy current probes are able to reliably and easily detect surface and subsurface discontinuities such as delamination and thickness variation. The probe setting parameters are described for different types of carbon composites (type of matrix and reinforcement, layup). Precise settings are necessary for the successful eddy current testing. It was determined that reliable detection of a minimum surface defect size is Ø1.5 mm for specimens and that eddy currents are able to penetrate into a thickness up to approximately 4 mm depending on the type of carbon composite. Additionally, this paper describes the comparison of eddy current testing with ultrasonic phased array method (PAUT). Composite aircraft structures are very susceptible to impact damage usually detected using PAUT. Therefore, sensitivity and resolution analysis of impact damage detection was performed by means of these two methods. It was found out that the results are very similar to each other when standard pulse-echo technique of PAUT was used. This carbon composite eddy current testing could fully replace or complement other NDT methods such as visual or ultrasonic inspections. Also it could guarantee the product quality in production, aircraft maintenance and in the development of new aircraft structures.

Operational modal analysis for damage detection in a rotating wind turbine blade in the presence of measurement noise

The present paper demonstrates the feasibility of detecting and quantifying crack-type damage features in rotating wind turbine blades by Operational Modal Analysis (OMA) under noisy measurement conditions. The excitation of blade vibrations is achieved through the rotationally sampled turbulence spectrum of the inflowing wind field. Accelerations are sampled at 30 locations along the blade. Given the high sensitivity of OMA-reconstructed mode shapes to the presence of noise a six-part methodology has been developed. It is demonstrated that small cracks can be detected reliably down to signal-to-noise ratios (SNR) at the blade tip of 40 dB, well below the SNR values achievable with commercial accelerometers and wind-excited acceleration amplitudes. Calibration curves for crack lengths and center positions have been constructed. The proposed methodology holds the potential for the implementation of robust and low-cost structural health monitoring systems for wind turbines at locations with difficult access, such as in an offshore environment.

Subdomain principal component analysis for damage detection of structures subjected to changing environments

Modal frequencies that capture structural dynamics are the most commonly used damage-sensitive features in vibration-based structural health monitoring. However, environmental changes usually lead to nonlinear frequency variabilities, which further mask the damage effects on modal frequencies. In this paper, a subdomain principal component analysis (Sub-PCA) method is proposed for damage detection of structures with robustness/immunity to environmental interference. Considering in mind the linear nature of PCA, it is performed in each of the disjoint linear subdomains of nonlinear frequency data. Two statistical analysis techniques including the Gaussian mixture model and the likelihood ratio test are adopted for subdomain division. Then damage indicators, i.e., the Mahalanobis and Euclidean distances, and their thresholds are calculated according to each Sub-PCA model. After that, weighted averages of the threshold-normalized indicators are defined to detect damages. Two case studies involving an experimental wooden bridge and an actual concrete bridge are employed to verify the effectiveness of the Sub-PCA method. The analysis results demonstrate that the Sub-PCA models, especially those constructed from the likelihood ratio test, are particularly robust/immune to changing environments in terms of damage detection.

Outlier analysis combined with Gaussian mixture model for structural damage detection

Damage diagnostic techniques based on changes in natural frequency has two major problems, limiting their applicability to practical structures. The limitations are low sensitivity to smaller-size damage and the influence of ambient and operational factors (temperature, humidity, wind, traffic, etc.). The change in natural frequency due to environmental and operational factors may be even larger than the change due to damage. A reliable vibration-based structural damage detection distinguishing damage and other confounding factors is needed to avoid false alarm of damage. This paper presents an enhanced approach combining the gaussian mixture model (GMM) and outlier analysis for damage detection considering the effect of changing environmental and operational conditions. In the proposed work, GMM is first applied to classify the feature vectors (first two natural frequencies) into different clusters. The natural frequencies obtained under similar environmental and operational conditions were classified into the same cluster by satisfying the same probability distribution. Outlier analysis is then performed for each different cluster using the Mahanabolis Distance (MD) measure. The MD is a measure of the distance between a point and a distribution. In the current study, the MD between the current data sample and the reference data of each mixture is computed and the minimum MD among all the clusters is used as the damage index (DI). The threshold to conclude the presence of damage (DI > threshold) is established based on Generalized Extreme Value Distribution using the datasets of the healthy structure with various environmental and operational conditions. The proposed method is verified using benchmark examples of numerically simulated simply supported beam and experimental wooden bridge structure. The results of the investigations concluded that the proposed DI considering the patterns in the data using GMM proves better in decoupling the damage and other confounding factors following the conventional outlier analysis of damage detection.

Continuous baseline update using recurrence quantification analysis for damage detection with guided ultrasonic waves

For the safe operation of vehicles and full utilization of lightweight materials, assurance of structural integrity is a prerequisite at all times. Structural health monitoring with permanently installed transducers offers a great advantage for primary load-bearing structures of all means of transportation and other safety-relevant components such as hydrogen tanks: allowing damage detection during operation. One means to detect internal defects is the method of guided ultrasonic waves (GUWs), which can be generated and recorded by piezoelectric transducers. GUWs propagate along the elongated dimension of a structure, and a transducer network can completely cover and monitor structures. Defects can alter the signal along affected paths and allow for their detection. However, a challenge and obstacle for the application of such a testing technique in the service of means of transportation is the large influence of temperatures. These influences are difficult to distinguish from the effect of defects. One approach to overcome this difficulty is the “continuous baseline update”. Recurrence quantification analysis is tested and compared to established features as a new approach to “continuous baseline update” in this paper. Publicly available GUW data (http://www.openguidedwaves.de/) recorded under varying temperature conditions have been used to show how the methods perform. They reliably separate temperature and damage effects, while the recurrence quantification analysis yields the best results.

Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment

Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment

Damage Management of Concrete Structures with Engineered Cementitious Materials and Natural Fibers: A Review of Potential Uses

The importance of the safety and sustainability of structures has attracted more attention to the development of smart materials. The presence of small cracks (<300 µm in width) in concrete is approximately inevitable. These cracks surely damage the functionality of structures, increase their degradation, and decrease their sustainability and service life. Self-sensing cement-based materials have been widely assessed in recent decades. Engineers can apply piezoresistivity for structural health monitoring that provides timely monitoring of structures, such as damage detection and reliability analysis, which consequently guarantees the service life with low maintenance costs. However, concrete piezoresistivity is limited to compressive stress sensing due to the brittleness of concrete. In contrast, engineered cementitious composites (ECC) present excellent tensile ductility and deformation capabilities, making them able to sense tensile stress/strain. Therefore, in this paper, first, the ability of ECC to partly replace transverse reinforcements and enhance the joint shear resistance, the energy absorption capacity, and the cracking response of concrete structures in seismic areas is reviewed. Then, the potential use of natural fibers and cellulose nanofibers in cementitious materials is investigated. Moreover, steel and carbon fibers and carbon black, carbon nanotubes, and graphene, all added as conductive fillers, are also presented. Finally, among the conductive carbonaceous materials, biochar, the solid residue of biomass waste pyrolysis, was recently investigated to improve the mechanical properties, internal curing, and CO2 capture of concrete and for the preparation of self-sensing ECC.

Compressive damage and heat release of composite laminate with circular prefabricated defect, part II-numerical method

In this paper, the compressive damage and its corresponding heat dissipation of carbon fiber reinforced polymer (CFRP) composite laminates with prefabricated defects are investigated by means of the numerical method. First, the relationship between the temperature change and the damage of laminate is derived. Second, both the damage evolution and the corresponding energy dissipation of composite laminates with the prefabricated interlaminate defect are simulated. The numerical results are consistent with the experimental results. Finally, influences of the circular prefabricated defect’s size are discussed by numerical models. These results play important roles in analyzing and evaluating damage of the composite laminates, which is significant for the damage detection and damage thermo-response analysis in engineering applications of composites.

Finite element modelling and analysis of damage detection in concrete beams using piezoelectric patches

In this present work quantification of damage index of concrete beam is carried out due to different types of cracks in concrete beam. The model of concrete beam with piezoelectric patches grooved inside is created by using finite element method (FEM). These piezoelectric patches act as actuators and sensors. ANSYS and MATLAB are used as tools for computation. Different concreate beam models created with cracks of varying vertical length and varying orientation of crack along with its length. Sensor data are decomposed using wavelet technique to estimate the damage index. It is observed that as crack length increases damage index also increases. In case of vertical crack, damage index is highest for different length of crack. The minimum damage index is observed for crack at 45° for different cases considered in this analysis.

Inverse analysis for damage detection in a rod using EMI method

The electro-mechanical impedance (EMI) method has been demonstrated to be a powerful tool to detect the small local damages in various engineering structures, due to its high-frequency vibration characteristics. However, this technique is difficult to accurately determine the degree and location of structural damages. In this paper, an inverse analysis combining the measured EMI signatures and the Transfer Matrix Method (TMM) is proposed to identify the damages in the steel rods. A milled groove is introduced into each steel specimen to simulate the local damage. By measuring the EMI signatures of the piezoelectric wafers bonded symmetrically on the upper and lower surfaces of the rectangular specimens respectively, the damage can be detected clearly, as well as natural frequencies being extracted in the relatively lower frequency range. The damage sizes and locations are further determined by connecting the frequency equations and any two measured natural frequencies. Tests on three samples show both damage sizes and locations are accurately detected, which indicates that the present method is quite feasible and effective for damage identification in the bar-type structures.

Kernel Principal Component Analysis for Structural Health Monitoring and Damage Detection of an Engineering Structure Under Operational Loading Variations

This paper highlights kernel principal component analysis (KPCA) in distinguishing damage-sensitive features from the effects of liquid loading on frequency response. A vibration test is performed on an aircraft wing box incorporated with a liquid tank that undergoes various tank loading. Such experiment is established as a preliminary study of an aircraft wing that undergoes operational load change in a fuel tank. The operational loading effects in a mechanical system can lead to a false alarm as loading and damage effects produce a similar reduction in the vibration response. This study proposes a non-nonlinear transformation to separate loading effects from damage-sensitive features. Based on a baseline data set built from a healthy structure that undergoes systematic tank loading, the Gaussian parameter is measured based on the distance of the baseline data set to various damage states. As a result, both loading and damage features expand and are distinguished better. For novelty damage detection, Mahalanobis square distance (MSD) and Monte Carlo-based threshold are applied. The main contribution of this project is the nonlinear PCA projection to understand the dynamic behavior of the wing box under damage and loading influences and to differentiate both effects that arise from the tank loading and damage severities.

Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven vibration-based structural health monitoring methodology. The blade’s debonding damage is introduced artificially, and the blade is excited with an electromechanical actuator that introduces a mechanical impulse causing the impact on the blade. The acceleration responses’ vibrations are measured by accelerometers distributed along the trailing and the wind turbine blade. A metamodel is formerly obtained through the Polynomial Chaos-Kriging method based on the damage indexes, trained with the blade’s healthy condition and four damage conditions, and validated with the other two damage conditions. The Polynomial Chaos-Kriging manifests promising results for capturing the proper trend for the severity of the damage as a function of the damage index. This research complements the damage detection analyses previously performed on the same blade.

Lamb Wave Scattering Analysis for Interface Damage Detection between a Surface-Mounted Block and Elastic Plate.

Since stringers are often applied in engineering constructions to improve thin-walled structures’ strength, methods for damage detection at the joints between the stringer and the thin-walled structure are necessary. A 2D mathematical model was employed to simulate Lamb wave excitation and sensing via rectangular piezoelectric-wafer active transducers mounted on the surface of an elastic plate with rectangular surface-bonded obstacles (stiffeners) with interface defects. The results of a 2D simulation using the finite element method and the semi-analytical hybrid approach were validated experimentally using laser Doppler vibrometry for fully bonded and semi-debonded rectangular obstacles. A numerical analysis of fundamental Lamb wave scattering via rectangular stiffeners in different bonding states is presented. Two kinds of interfacial defects between the stiffener and the plate are considered: the partial degradation of the adhesive at the interface and an open crack. Damage indices calculated using the data obtained from a sensor are analyzed numerically. The choice of an input impulse function applied at the piezoelectric actuator is discussed from the perspective of the development of guided-wave-based structural health monitoring techniques for damage detection.

Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions

Damage detection of civil engineering structures needs to consider the effect of normal environmental variations on structural dynamic properties. This study develops a novel structural damage detection method using factor analysis in the sparse Bayesian learning framework. The unknown changing environmental factors that affect the structural dynamic properties are treated as latent variables in the model. The automatic relevance determination prior is adopted for the factor loading matrix for model selection. All variables and parameters, including the factor loading matrix, error vector and latent variables, are solved using the iterative expectation-maximization technique. The variables are then used to reconstruct structural responses. The Euclidean norm of the error vector is calculated as the damage indicator to detect possible damage when limited vibration data are available. Two laboratory-tested examples are utilized to verify the effectiveness of the proposed method. Results demonstrate that the number of underlying environmental factors and structural damage can be accurately identified, even though the changing environmental data are unavailable. The proposed method has the advantages of online monitoring and automatic identification of underlying environmental factors.