Structural Damage Localization Research Articles

Vibration-based structural damage localisation using time response correlation

ABSTRACT A Damage Indication Function (DIF) is developed in this work to localise the damage on a structure. The novelty is that this is the first work proposing a time response correlation-based damage localisation method. To identify the damage, dynamic time response is measured by several sensors placed on the structure using a user excitation. A set of damaged finite element models is developed. Each damage model has only one specified damage but at different locations. The collection of the damage models forms a complete damage grid. The same user excitation is applied to each damage model to generate an analytical response. The test response is then correlated with the model response of each damage model. The highest correlation between the test response and the model response of all damage models would indicate the damage location on the test structure. A plate-type structure is used to demonstrate the power and effectiveness of DIF experimentally.

Modal analysis and damage localization in plate-type structures via TDD and PE methods based on the data of an integrated highspeed camera system

With the fast development of full-field vibration measurements by combining digital image correlation (DIC) technique with the integrated highspeed camera system, the structural characteristic deflection shapes (CDS’s) can be readily captured, which are sensitive for damage localization. Naturally, the mode shapes are preferred due to their higher signal to noise ratio. However, traditional modal analysis methods are inefficient in processing the large volumes of data that acquired by high-speed camera system for mode shape-based damage localization. Moreover, how to extract the damage features and enhance the damage localization results without the baseline-data of the intact state is another challenging issue. To address these problems, a novel damage localization method is proposed by enhancing the mode shape estimation and optimizing the damage feature extraction. Firstly, the dominant singular vector at each interested resonant frequency is individually evaluated as the mode shapes in the proposed time domain decomposition (TDD) method. Moreover, based on the extracted mode shapes, a pseudo-excitation (PE) method is proposed to reveal the damage locations without baseline-data by examining the perturbation of local dynamic equilibrium. To circumvent the effects of measurement noise on PE method, an adaptive denoising technique is proposed based on Gaussian smoothing. Furthermore, the local contiguity and spatial sparsity of the damage-caused features are harnessed by a hierarchical clustering to optimize the damage localization performance via tuning the scale parameter of Gaussian smoothing. Finally, numerical and experimental studies of crack damaged plates are conducted to validate the feasibility and effectiveness of the proposed mode shape estimation and damage localization methods. During the experiment, the full-field vibration measurements are acquired by using an integrated binocular highspeed camera system. In addition, a comparison study based on benchmark data is carried out to demonstrate the performance and reliability of the proposed damage localization method.

Two-stage damage identification method based on fractal theory and whale optimization algorithm

Structural damage identification based on time domain method of vibration response has been widely developed in the recent decades, however, it still confronts some difficulties, such as measurement noise and model error. This paper proposes a novel two-stage damage identification method based on fractal dimension and whale optimization algorithm (WOA). In this study, based on vibration data, the difference in curvature of fractal dimension (DCFD) is used as the damage index to identify the location of suspicious damage elements in the first stage. A new objective function is proposed based on the curvature of fractal dimension (CFD) of acceleration signal, and the WOA is used to estimate the severity of the suspicious damaged element in the second stage. Firstly, the validity of the proposed method is verified by a numerical simply supported beam, and the results exhibit good damage identification ability. Then different noise levels (5% ~ 20%) are introduced into the dynamic responses to verify its robustness, the result shows that the method is of good anti-noise ability in the first stage. Although the second stage is slightly sensitive to noise, it can still effectively identify the severity of damage. Secondly, the vibration testing of a steel I-beam is designed to verify the rationality of the method in the application of actual structure. Finally, based on the simulated vibration test data of the I-40 Bridge, the applicability of the method to complex civil structure is verified, which shows that the method still has good ability to identify the location and severity of damage in complex structure and is of great significance in practical application.

Detection, Location and Quantification of Structural Faults in a Two-Story Building Using the Artificial Immunological System

Large buildings and skyscrapers are vulnerable to environmental, temporal, and anthropological stresses, generating wear and tear that can lead to this social and economic impediment's collapse. The technological improvements of the fourth industrial revolution have resulted in changes in the connection between physical space and man, known as the cyber physic model, which necessitates monitoring systems to protect the structural branch and so correct this structural vulnerability. Thus, the structural health monitoring system is the exact measure of the evolution required by the cyber physic model in construction and the protection of the monumental buildings, ensuring not only their economic development but also the safety of society. Therefore, this research work presents the innovative proposal of the cyber-physical structural health monitoring system aimed at buildings and skyscrapers, based on and differentiated by intelligent computing techniques, using the negative selection algorithm to perform the analysis and monitoring of structural integrity, overcoming the existing traditional work. The cyber-physical structural health monitoring system will be applied to experimental data obtained from the shear building model that represents these imposing skyscrapers. An artificial immune system will be developed and used in the decision-making process based on the acquisition and processing of the obtained signals to perform the identification, localization, and quantification of possible structural damage. Observing the results, this work proved to be efficient, robust, and economically feasible, having high performance and overcoming the shortcomings of traditional techniques. It represents the perfect measure of cyber physics in the monitoring of large buildings and skyscrapers.

Damage localization for prefabricated bridges group using the area-ratio of the strain time-history curve

The prefabricated bridges group refer to several medium- and small-span prefabricated beam bridges locating in a continuous elevated corridor. It is usually challenging to accurately localize the damage in all bridges within one group due to the lack of monitoring data of bridges in the healthy state. To address this issue, a damage localization method for prefabricated bridges group is proposed based on the area-ratio of the strain time-history curve. First, using the definition of the bridge strain influence line, an area equation of the strain time-history curve is derived theoretically under a moving load for a simple beam bridge and a continuous beam bridge. Second, a damage localization index is established based on the area-ratio of the strain time-history curve by analyzing the constitutive characteristics of the area equation of the strain time-history curve at each measurement point. On this basis, a normalization method is presented for the damage localization index at all measurement points, and then the normalized index is implemented to accurately localize damage in all bridges. Finally, the effectiveness and anti-noise performance of the proposed method are demonstrated through both numerical examples and experiment. The proposed method does not need historical health data or a finite element model (FEM) as the reference and can transform strain time-history curves with different amplitudes from different positions along bridges into a unified normalized index that relates to structural stiffness changes only, which is especially suitable for structural damage localization of long prefabricated bridges group.

Non-destructive damage localization in built-up composite aerospace structures by ultrasonic guided-wave multiple-output scanning

This paper focuses on defect localization in a composite skin-to-stringer stiffened panel representative of modern aircraft construction by an ultrasonic scanning system using multi-channel transfer function extraction. An especially made mini-impactor is utilized as the excitation to generate a broad wave frequency range. At each scanning position, multiple excitations are concatenated to extract the Guided-Wave Structural Transfer Function (GWSTF) between two points of the test structure. This extraction is performed using an averaged, normalized cross-power spectrum operation that minimizes the influence of the excitation and that of uncorrelated noise. The GWSTF extracted from different sensor pairs is then related to the presence of damage at different locations. The system is thus able to locate damage in the panel in 2D (along and across the stringer direction), in a substantial improvement over a 1D scanning system developed by the authors previously. The system is tested on damage in the form of added masses and real damage in the form of through-thickness stringer notches. The results show good detection and location of damage, suggesting the potential for a practical inspection system targeting internal damage in aircraft structures when probed from the outer skin side.

Structural damage identification employing hybrid intelligence using artificial neural networks and vibration-based methods

The basis of vibration-based damage detection method is that when there are changes in physical properties of a structure, there will also be changes in vibration characteristics such as natural frequencies, mode shapes and damping ratios. Artificial neural network has become one of the most powerful approaches capable of pattern recognition, classification, and nonlinear modelling employing computational intelligence techniques to tackle damage detection as a complex problem. In this paper, ensemble neural networks based damage identification techniques were developed and applied for damage localization and severity identification in I-beam structures using dynamic parameters. Experimental modal analysis and numerical simulations of I-beam with triple-point damage cases were carried out to generate the natural frequencies and mode shapes of structures. In the procedure of damage identification, five different neural networks corresponding to mode 1 to mode 5 were constructed, and then an approach based on a neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble neural network has the superiorities of all the individual networks from different vibrational modes. The ensemble network produced better damage identification outcomes than the individual networks and the results revealed the ability of the ensemble neural networks to identify damage.

Localization and classification of structural damage using deep learning single-channel signal-based measurement

Diligent damage identification is a core thrust of structural health monitoring (SHM). Vibration-based SHM has recently gained paramount importance. Substantial research devoted to Deep Learning (DL) algorithms yielded superior accuracy. This study proposes a novel DL-based damage detection approach to automatically extract features from raw acceleration sensor data. A new One-Dimensional Convolutional Neural Network named BuildingNet was designed to learn features and identify damage locations in real-time under different damage assessment scenarios. Parametric studies were conducted on different layer numbers, size of training datasets, and noise levels. Systematic studies for optimizing the network architecture and training data were performed. Mid-rise building case studies demonstrated the accuracy and efficiency of the proposed model. Time-domain monitoring data, both from multiple and single-channel measurements, were used for training and testing different architectures of BuildingNet. The proposed model achieved excellent damage localization and classification performance on both noise-free and noisy data sets.

Lamb wave imaging based on multi-frequency sparse decomposition

Based on the assumption of damage sparsity, scattering signals can be directly sparsely decomposed in the time domain to obtain the scattering coefficients to generate images for indicating damage, which has been comprehensively studied in recent years. However, in these previous studies, the dimension of the constructed dictionary matrix is proportional to the product of the number of discrete imaging points and the signal length, which will cause the “dimensional disaster” problem. Considering the energy and useful information of the received Lamb wave signals are limited within a certain frequency bandwidth, a Lamb wave imaging method based on multi-frequency sparse decomposition in the frequency domain is proposed for damage localization in isotropic plate-like structures. A sparse representation model for the scattering signals in the frequency domain is established based on the constructed frequency domain dictionary matrix. Each single frequency component of the scattering signals obtained from the sensor array is sparsely decomposed one by one to obtain the frequency-dependent scattering coefficients. After that, images indicating damage locations can be generated by fusing the multi-frequency scattering coefficients. Both numerical simulations and experiments on aluminum plates are implemented to validate the proposed method, which provides an alternative tool for Lamb wave based damage localization. Results with a higher signal-to-noise ratio, fewer artifacts, and lower background noise are obtained compared with the conventional delay-and-sum and minimum variance distortionless response methods.

Damage localization for bridges monitored within one cluster based on a spatiotemporal correlation model of strain monitoring data

The bridges monitored within one cluster refer to several medium- and small-span beam bridges with similar structural characteristics located in a continuous elevated corridor. The variation in the strain monitoring data of these bridges comprehensively reflects diverse coupling effects. These complex coupling factors present great challenges for the damage diagnosis of bridges. To address this issue, a damage localization method for bridges monitored within one cluster is proposed based on a spatiotemporal correlation model of strain monitoring data between bridges. First, a deep learning architecture combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network is established, which can reveal the complex time-varying mapping relationship between the strain monitoring data for similar bridges within one cluster to obtain an accurate spatiotemporal correlation model. Second, a strain prediction framework is presented that uses the proposed spatiotemporal correlation model after training. On this basis, the predicted and measured strains can be utilized to calculate a damage localization index that is not affected by complex coupling factors. Then, combined with abnormal diagnosis theory, the proposed index is implemented to accurately localize damage in all bridges within one cluster. Finally, the application of the proposed method to three actual bridges monitored within one cluster demonstrates the accuracy of the spatiotemporal correlation model and the effectiveness of structural damage localization.

Seismic performance of mid-rise reinforced concrete buildings in Izmir Bayrakli after the 2020 Samos earthquake

On October 30th 2020, offshores of Samos Island, Greece was hit by a shallow earthquake of Mw = 6.9 moment magnitude. Located 76 km away from the earthquake’s epicenter, the mid-rise reinforced concrete (RC) building stock of the densely populated Izmir Bayrakli district experienced the most severe structural damage. Although the horizontal peak ground acceleration recorded at the site (PGA = 0.11 g) was well below design values recommended by the past and the present Turkish seismic codes, many buildings suffered heavy damage and 12 buildings collapsed, resulting in a death toll of 117. This paper aims to identify the seismic performance of mid-rise infilled RC frame buildings in Bayrakli by investigating the ground motion recordings, local soil conditions, characteristics of the building stock and the observed damages. The structural performance of 140 RC buildings between 7 and 12 stories that have exhibited poor performance during the earthquake was inspected in detail based on the field surveys conducted right after the earthquake. A database was created documenting the number of stories, major seismic deficiencies, date of construction, geographic coordinates, damage state of structural members and masonry infill walls of the inspected buildings. The localization of structural damage could be partially attributed to the amplified long-distance seismic waves for 0.5–1.5 s periods range due to local soil conditions and basin effects. Seismic vulnerabilities, structural deficiencies and construction quality of the buildings played a significant role in the experienced damage intensity. The influence of infill walls on the seismic performance of RC frame buildings is also clearly observed for code-compliant as well as aseismic buildings.

Structural Damage Localization under Unknown Seismic Excitation Based on Mahalanobis Squared Distance of Strain Transmissibility Function

Due to the unpredictability of seismic excitation, the data-driven damage identification method, which only depends on the monitoring response data, has a good development prospect in structural health monitoring. In recent years, damage identification methods based on transmissibility function (TF) and Mahalanobis squared distance (MSD) have been widely studied. However, the existing methods are only applicable to damage warning. To overcome this limitation, an improved method for structural damage localization is proposed. Strain TF is used to eliminate the influence of unknown ambient excitation and unknown seismic excitation, which is more sensitive to local damage than traditional TF based on acceleration and displacement data. The MSD of strain TF is employed to construct a novel damage indicator that is used to identify the damage location. Two numerical simulations have been conducted to verify the feasibility of the method for damage localization and good anti-noise performance. In the case of the multi-damage condition, the novel damage indicator is performed to estimate the severity of damage to some extent.

Research on Bridge Structural Damage Identification

The traditional identification methods have limited ability to identify damage location of bridge structures. Therefore, a bridge structural damage location identification method based on deep learning is proposed. In addition, the sigmoid function is the activation function, and the cross entropy is the cost function. Meanwhile, take the Gaussian noise as the addition method and take the softmax as the classifier. So the constructed SDAE deep learning model can realize damage location identification of the simply supported the continuous beam bridges. Compared with the traditional identification methods of bridge structures, namely BP network and SVM, the proposed method shows higher identification accuracy and antinoise performance. Here, the average identification accuracy of the method for continuous beam bridge is 99.8%. As can be seen that the proposed method is more suitable for practical bridge structure damage location identification.

Microbial Community Composition in Explanted Cystic Fibrosis and Control Donor Lungs

To date, investigations of the microbiota in the lungs of people with Cystic Fibrosis (PWCF) have primarily focused on microbial community composition in luminal mucus, with fewer studies observing the microbiota in tissue samples from explanted lung tissue. Here, we analysed both tissue and airway luminal mucus samples extracted from whole explanted lungs of PWCF and unused donor lungs. We determined if the lung microbiota in end-stage CF varied within and between patients, was spatially heterogeneous and related to localized structural damage. Microbial community composition was determined by Illumina MiSeq sequencing and related to the CF-Computed Tomography (CT) score and features of end-stage lung disease on micro-CT. Ninety-eight CF tissue (n=11 patients), 20 CF luminal mucus (n=8 patients) and 33 donor tissue (n=4 patients) samples were analysed. Additionally, we compared 20 paired CF tissue and luminal mucus samples that enabled a direct “geographical” comparison of the microbiota in these two niches. Significant differences in microbial communities were apparent between the 3 groups. However, overlap between the three groups, particularly between CF and donor tissue and CF tissue and CF luminal mucus was also observed. Microbial diversity was lower in CF luminal mucus compared to CF tissue, with dominance higher in luminal mucus. For both CF and donor tissue, intra- and inter-patient variability in ecological parameters was observed. No relationships were observed between ecological parameters and CF-CT score, or features of end-stage lung disease. The end-stage CF lung is characterised by a low diversity microbiota, differing within and between individuals. No clear relationship was observed between regional microbiota variation and structural lung damage.

A Multi-Excitation Method of Damage Detection in Plate-Like Structure Based on Wavelet Packet Energy

Damage detection technology based on wavelet packet energy has been widely applied to structural health monitoring, especially for one-dimensional structures. With more complex vibration characteristics than one-dimensional structure, two-dimensional structure is common in ship structure. It is a challenge for using wavelet packet energy methods to detect damage in two-dimensional structures. In this study, based on the wavelet packet energy eigenvector, a novel concept of detection index ratio (DIR) is defined to characterize the damage location ability of the excitation force. Based on the above research, a multi-excitation method of damage location in plate-like structures is proposed. In this method, a series of half-sine pulse excitation forces with different frequencies are applied to the structure, and wavelet packet transform (WPT) is used to decompose the vibration response of the intact structure and the damaged structure. The energy ratio changes in frequency bands on all measuring points are taken as the preliminary damage detection index, and the final detection index is obtained by synthesizing the preliminary indexes under various excitation. Moreover, to verify the effect of the proposed method, several tested damage cases are presented. The results show that the proposed method can locate single and multiple damage more accurately and has better noise robustness than the traditional detection method under single excitation.

Structural Nonlinear Damage Identification Using the Residual Deviation Distance Conversion Index of the Time-Domain Model

Steel structures such as transmission towers may have cracks and other damages during the service period. When the structure vibrates, the cracks will open and close, which makes the structure have nonlinear characteristics of variant stiffness. In order to identify this type of nonlinear damage, a time-domain damage identification method based on the residual deviation distance (RDD) of the autoregressive conditional heteroskedasticity (ARCH) model is proposed. First, the basic theory of the ARCH model is introduced, and the methods of model order determination and parameter estimation are given. Then, the time-domain nonlinear damage characteristics are analyzed, and the second-order variance (SOV) index based on the ARCH model is introduced. Considering that both the ARCH model coefficient and the RDD contain nonlinear information, a conversion index is constructed using the Euclidean distance (ED) of the ARCH model coefficients before and after the damage. On this basis, an RDD conversion index is further proposed to improve the identification accuracy. Finally, a three-storey frame experiment and a complex transmission tower model experiment are used to verify the effectiveness of the proposed method. The experimental results show that the proposed RDD conversion index can better identify the location of structural damage, and the identification results of the RDD conversion index are obviously better than those of the SOV conversion index and the ED conversion index.

Serious Damage Localization of Continuous Girder Bridge by Support Reaction Influence Lines

A novel damage detection approach is proposed in this study for a continuous girder bridge in which support reaction influence lines (ILs) are adopted. First, the relationship between the local damage of a continuous girder bridge and a damage index, based on support reaction ILs, is established through analytical derivation. Subsequently, the sensitivity of a support reaction IL-based damage index is analyzed using Dempster-Shafer (D-S) evidence theory, and it shows that the support reaction IL-based damage index is more noise-resistant if more support reaction ILs from a variety of locations are used. Three case studies (a simple numerical study of a two-span continuous beam, a laboratory experimental study of a two-span aluminum beam, and a complicated numerical study of a continuous girder bridge in Xiamen) have been conducted to validate the effectiveness of the proposed method in different damage scenarios, including single damage and multiple damages. Satisfactory damage identification results can be obtained even in high-level measurement noise conditions, showing that the proposed approach offers a promising field detection technique for identifying local structural damages in continuous girder bridges.

Realization and application of an improved multi-fold method for ultrasonic guided wave

The collected ultrasonic guided waves (UGWs) superimpose several interference signals in the structural damage localization, which severely affects the localization accuracy. The multi-fold method for seismic wave was tried introducing into UGWs to overcome the distortions of ultrasonic signals in the traditional filtering process after being improved. The improved multi-fold (IMF) can be described as follows: UGWs, which are collected repeatedly for many times, are superimposed to obtain their average values using a weighted average algorithm after their arrival times and drifted zero points have been corrected. During the application for echo signal, the denoising effect of the IMF method improves with the increase of the superposition times. Through the practical application in the damage localization of steel pipeline, the IMF method for UGW is confirmed to be feasible, excellent and stable.

Damage Identification for Shear-Type Structures Using the Change of Generalized Shear Energy

Structural damage identification has become an important topic in the field of civil engineering in recent years. The shear-type structure, such as shear frame structure, is a common type used in civil engineering. In this paper, a damage identification method based on the change of generalized shear energy is proposed for shear-type structures. The main steps of the proposed method are as follows. Firstly, the element stiffness matrix in the structural finite element model is decomposed to obtain the elementary shear force vector. Secondly, the elementary generalized shear energy is calculated by the dot product of the vibration mode shape vector and the elementary shear force vector. Thirdly, structural damage locations can be determined by the changes of elementary generalized shear energy. Finally, more accurate damage localization and quantification are achieved by solving the mode shape sensitivity equation. A 20-storey numerical example and a three-storey experimental model are used to demonstrate the proposed damage identification algorithm. From the numerical and experimental results, it was found that the proposed approach can accurately identify the location and extent of the damage in the shear structures even if the data contain noise. It has been shown that the presented algorithm may be useful in the damage identification of shear-type structures.

Diagnosis of internal cracks in concrete using vibro-acoustic modulation and machine learning

This paper investigates the utility of physics-informed machine learning models for vibro-acoustic modulation (VAM)–based damage localization in concrete structures. Vibro-acoustic modulation is a nonlinear dynamics-based non-destructive testing method, which was initially developed to perform damage detection and later extended to accomplish damage localization. The VAM-based damage (hidden crack) diagnosis is performed by analyzing the damage index pattern on the surface of the component to arrive at the size and location of the hidden damage. Past investigations have employed heuristically selected damage index thresholds as well as computationally expensive Bayesian estimation methods for VAM-based damage localization in two (surface) dimensions. Compared to these studies, the proposed methodology automates the threshold selection (algorithmic instead of heuristic), increases the speed of the probabilistic damage diagnosis process, and enables the estimation of damage depth. We generate training data (damage index) for the machine learning models using the pertinent nonlinear dynamics (finite element) models using different combinations of test parameters. The (supervised) machine learning models are thus informed by computational physics models. These include two types of artificial neural network (ANN) models: classification models that identify whether a sensor location is damaged or not and regression models that enable Bayesian estimation to obtain the posterior probability distribution of damage location and size. The accuracy of machine learning-based diagnosis is evaluated using both numerical and laboratory experiments. The proposed physics-informed machine learning models for VAM-based damage diagnosis are able to achieve an accuracy of about 60–64% in the validation experiments, indicating the potential of these methods for internal crack detection. The results show that for complex (nonlinear dynamics-driven) diagnostic methods, damage index patterns learned from physics models could be successfully used for damage detection as well as localization.