Structural Damage Detection Research Articles

Influence Line Identification Method Based on VMD Combined with Improved Wavelet Threshold Denoising

The influence line of bridge can reflect the performance of the bridge under the moving vehicle load. It has a wide range of applications in structural damage detection, performance evaluation, model correction and bridge weigh-in-motion. Fast moving vehicles will cause the dynamic effect of the bridge, resulting in the change of the bridge response curve and the difficulty of identifying the influence line. In this paper, an influence line identification method based on variational mode decomposition (VMD) combined with improved wavelet threshold denoising is proposed. Firstly, the mathematical model of influence line identification is established based on vehicle load matrix and bridge response. Then, VMD combined with improved wavelet threshold denoising method is used to eliminate dynamic fluctuation effect and noise. Finally, the elastic net penalty term is introduced in the influence line identification problem, and the B-spline basis function is used to reconstruct the influence line. In order to verify the effectiveness of the proposed method, a vehicle-bridge coupling model is established for numerical calculation, and the identification of bridge displacement influence lines under different conditions such as vehicle speed, noise level, road roughness and vehicle weight is investigated. The results show that the proposed method can effectively eliminate the dynamic fluctuation components and noise of the bridge response. In the case of good road roughness, the influence line can still be accurately identified when the vehicle speed reaches 15 m/s.

Musculoskeletal ultrasonography in rheumatic diseases.

Ultrasonography is an imaging technique based on sound waves used for the evaluation of soft tissues. Sound waves have been used for a long time in nonmedical fields, including military defense systems, radar systems, and detection of icebergs. Technological advances resulted in new techniques becoming available for medical imaging, including ultrasonography, magnetic resonance imaging, and computed tomography. Nowadays, the use of imaging has become a gold standard protocol in the diagnosis of many diseases, and recently developed diagnosis and therapy options provide more efficient treatment of rheumatic diseases. Thus, it has become possible to prevent structural damage and disability in patients with rheumatic disease. Musculoskeletal ultrasonography is becoming a preferred imaging technique for rheumatic diseases, as it has many advantages. Among its advantages are being inexpensive, being radiation-free, having a dynamic image capacity, helping to detect disease activity, and helping with early detection and diagnosis of structural damage. This review summarizes the use of ultrasonography in rheumatic diseases.

Time Series Analysis Methodology for Damage Detection in Civil Structures

Structural health monitoring (SHM) methodologies employing data-driven techniques are becoming increasingly popular for detection of structural damage at the earliest stage possible. With measured vibration signals from the structure, time series modeling methods provide quantitative means for extracting such features that can be utilized for damage diagnosis. In this study, one-step prediction error of an autoregressive (AR) model over a data set is used as damage indicator. In particular, the difference between the prediction of the AR model that is fit to the measured acceleration signal obtained from the intact structure and actual measured signals collected for different damage states of the structure are interrogated for diagnosis purposes. More specifically, the standard deviation of the residual error is employed to locate the damaged region. Singular-value decomposition (SVD) is employed to find the optimal order for an AR model created using the impulse responses of the system. Numerical simulations are carried out using the impulse responses acquired from a four-story frame structure contaminated with additive noise including single and multiple damaged elements. The results of the simulations demonstrate that the method can be effectively employed to detect and locate damage. The performance of the proposed procedure are further demonstrated using the impact data acquired from a reinforced concrete frame for real applications.

Online damage monitoring of inelastic seismic response of a reinforced concrete structure

Methods of early warning in damage detection of civil engineering structures represent a particular challenge in Structural Health Monitoring (SHM). To investigate these problems, vibration records of a full-size, reinforced concrete frame subjected to seismic damages on a shaking table, were investigated. The seismic recordings of excitations and response were accompanied by images of cracks associated with each state of the damage. This paper presents the results of the damage identification from non-linear response during the damaging phases of the shaking table tests. Time-dependent base shear forces were observed, together with wavelet spectral analyses of the responses. Three damage-sensitive features accounting for decreasing stiffness, energy dissipation, and the wavelet changes during inelastic seismic responses were analyzed as functions of horizontal peak table velocities. The paper aims to study seismic response records of moment-resisting reinforced concrete structure frames in search of prospective simple online damage indicators which could be applied to start alarms, right after excessive damage loads, e.g., in instantaneous earthquake rapid damage assessments of buildings.

An Output-Only, Energy-Based, Damage Detection Method Using the Trend Lines of the Structural Acceleration Response

Using the trendlines of an acceleration response as a tool to decompose a structural response is a new topic that was proposed by authors in 2020. This paper provides a numerical/experimental investigation of using a Savitzky–Golay filter (SGF) in a method to calculate the trendline and decompose building acceleration responses when subjected to a seismic load. Hence, this paper proposes an output-only, energy-based, damage detection method in which the trend lines of a building’s structural acceleration responses are used to locate the damage. For this purpose, an adjusted SGF is utilized to calculate an especial trend line for each floor’s acceleration response of the building structural model. The energy of these trend lines is then calculated and normalized. Two damage indices are used, of which, the second one is being proposed for the first time in this paper. The accuracy of the proposed method is numerically and experimentally investigated using a five-floor building structural model subjected to white noise excitation through a shake table. The results prove that the proposed method is capable of accurately locating and quantifying structural damages with a severity of more than 10% in a noisy environment. In view that the proposed method locates the damage with no need of determining the structural modal properties or parameters, it can be categorized as an online and quick structural damage detection method.

Output-only structural damage identification based on Q-learning hybrid evolutionary algorithm and response reconstruction technique

Various structural damage identification methods have been developed and employed, while the absence of input excitation measurements may pose a huge challenge in their application since the input forces such as seismic load, traffic load, wind load, are not directly measurable. To address this issue, in the present paper, a novel output-only structural damage detection approach based on Q-learning hybrid evolutionary algorithm (QHEA) and response reconstruction technique is presented. On the one hand, the external excitation and structural acceleration responses are reconstructed with the aid of response reconstruction and Tikhonov regularization techniques in time domain. Structural damage identification can be formulated as an optimization-based inverse problem. On the other hand, a new optimization framework QHEA integrating Jaya algorithm, differential evolution, Q-learning algorithm is developed as search tool. The unknown structural parameters and unmeasured input force are iteratively updated by using two different measurement sets until the reconstructed acceleration responses agree well with the measured responses. Numerical examples involving a cantilever beam structure under single excitation and a simply-supported 51-bar truss structure under multiple excitations, as well as an experimental five-floor steel frame structure in the laboratory are carried out to validate the effectiveness of the proposed method. The final results demonstrate that the proposed method can accurately detect damage locations and quantify damage extents without the information of input excitation. In addition, the superiority of QHEA over other heuristic algorithms, the uncertainties of measurement noise and modeling errors on damage identification results are further examined.

A novel damage detection method for carbon fibre reinforced polymer structures based on distributed strain measurements with fibre optical sensor

This paper presents a novel approach for damage detection in carbon fibre reinforced polymer (CFRP) structures based on local strain feature matching. The proposed approach utilizes distributed in-plane strain measurements for plate structures under load (in-service application) to define a local point autocorrelation coefficient based on Delaunay triangulation and Hausdorff distance. This coefficient effectively identifies the discontinuities in distributed in-plate strain, enabling accurate capture of damage information through the damage index which is defined as the integral of this coefficient. Notably, the proposed approach requires no prior knowledge of the non-damaged strain distribution and yields excellent detection results for both Barely Visible Impact Damage (BVID) and Visible Impact Damage (VID). Leveraging the advantages of distributed strain sensing, coupled with image processing technology for the purpose of in-service damage detection of CFRP structures, the proposed novel method is a promising technology. The efficacy of this technology has been demonstrated in this paper through theoretical analysis and experimental investigations, offering valuable insights and reference for future research in this domain.

Damage detection of CFRP laminates based on Adamax-INN model for EIT

Carbon fiber reinforced polymer (CFRP) is electrically conductive, making it possible to detect structural damage of CFRP laminates with electrical impedance tomography (EIT). However, the inverse problem of EIT is severely non-linear, ill-posed, and underdetermined, thus limiting the resolution and accuracy of images reconstructed with EIT. This paper solves the inverse problem of EIT based on invertible neural networks (INN) and optimizes INN with Adamax gradient descent algorithm. Simulation and experimental results demonstrated that, compared with traditional EIT image reconstruction algorithms and radial basis function (RBF) neural network algorithm, the Adamax-INN model largely reduced the artifacts in reconstructed images, improved the damage recognition accuracy and edge clarity, and displayed good noise immunity.

Fundamental mode shape-based damage detection in arbitrary shaped plates using a hybrid of contourlet transform and Delaunay triangulation

The past studies have been focused on the field of the damage detection of rectangular plate structures. A major limitation of these studies is that the proposed methods can’t be used for arbitrary shaped plate structures. The main aim of this study is to present a new methodology based on a hybrid of the contourlet transform and the Delaunay triangular for the damage detection in arbitrary shaped plate structures. In the proposed method, the fundamental mode shape of arbitrary shaped plate is first simulated as a 2-D spatially-distributed signal. To achieve this purpose, the intact and damaged plates are separately inserted into a virtual rectangular, and the responses of the fundamental mode shape for the virtual rectangular plates are exploited by the Delaunay triangular as a most popular interpolation method. Then, the contourlet transform is applied to the mode shapes of intact and damaged plates. Finally, the location of damages is identified by a damage index which is obtained using the coefficients of the contourlet transform. The efficiency of the proposed method are investigated for several plate structures with different shapes and an experimental study. Numerical and experimental results show that the proposed method is capable of identifying the location of damages in rectangular and arbitrary shaped plate structures. It also appears that this method can reveal the location of one or multi-damages, and the main shape of actual plates.

VIBRATION-BASED DAMAGE DETECTION FOR ONE-STORY STEEL FRAME STRUCTURE USING MODE SHAPE CURVATURE

Structural health monitoring techniques, particularly vibration-based damage detection, have gained significance in assessing civil structure condition. This paper focuses on utilising mode shape curvature for damage detection in a one-story steel frame structure. The study aims to overcome traditional inspection limitations by exploring vibration-based approaches. Experimental investigation is conducted to analyse intact and damaged structural modal behaviour. Modal analysis technique extracts modal frequencies and mode shapes, enabling analysis of mode shape curvature for damage detection and localisation. Preliminary findings show that damaged structures display deviations in mode shapes and reduced natural frequencies, providing evidence of structural damage. However, a significant issue arises near the support, where unexpected patterns emerge in the Total Damage Index (TDI) with increasing damage severity. This finding challenges the expected correlation between severity levels and TDI values, highlighting the need to consider factors like fixed supports. Misleading signs of damage in some segments underscore the importance of cautious result interpretation and accounting for noise. Future studies should focus on noise resistance, false indication mitigation, and understanding segments with fixed supports to enhance mode shape curvature analysis’s reliability for damage detection in civil structures.

Vibration-based damage localization in 3D sandwich panels using an irregularity detection index (IDI) based on signal processing

As 3D sandwich panels have become prevalent in construction projects, developing structural damage detection approaches is of great significance to ensure their integrity. This study aims to introduce a damage identification method to diagnose damaged regions in 3D sandwich panels using modal dynamic data. To accomplish this, an Irregularity Detection Index (IDI) is defined based on the horizontal, vertical, and diagonal detail coefficients of the wavelet analysis of an input signal consisting of primary and secondary mode shapes. The relative maximum values of irregularities on the plotted IDI signal indicate damaged locations. It is also shown that with an increase in the damage severity at a damaged location, its corresponding IDImax increases, while changes in IDImax for other undamaged locations are negligible. In fact, the detection or non-detection of a damaged location does not affect the damage localization process in other locations.

Phase/frequency analysis of diffuse Lamb-wave field for fatigue-crack detection in an aluminium multi-riveted strap joint aircraft panel

A diffuse Lamb-wave field is utilised for fatigue-crack detection in a multi-riveted strap-joint aircraft panel. The work involves an extensive experimental programme of fatigue testing. Ultrasonic Lamb-wave responses are captured using a network of low-profile, surface-bonded piezoceramic transducers. Various phase/frequency characteristics of Lamb waves are used to detect and monitor fatigue cracks. An outlier statistical analysis – based on these characteristics - is also performed for damage detection. The results show that phase analysis can be used successfully for damage detection in such structures. In contrast, frequency analysis does not offer reliable damage detection. The results demonstrate the potential and limitations of the method for reliable fatigue-crack detection in complex aircraft structures.

An FDD-based modal parameter-less proportional flexibility-resembling matrix for response-only damage detection

Modal flexibility-based methods are effective tools for vibration-based structural damage detection, including in the output-only case. These methods are typically characterized by two stages: first, the modal parameters are identified, thus obtaining a certain number of modes; second, these modal parameters are used to assemble the modal flexibility matrix. This paper proposes a method for estimating a matrix that approximates a proportional flexibility matrix, termed proportional flexibility-resembling (PFR) matrix, and shows that this matrix can be used for damage detection and localization purposes. This matrix is obtained through signal processing operations to be executed after applying the first steps of the frequency-domain decomposition (FDD) technique—i.e., after the singular value decomposition of the spectral density matrix. The defining aspect of the PFR matrix is that, differently from the traditional formulation of modal flexibility and proportional flexibility matrices, it can be assembled without the need of an explicit identification of the modal parameters. In fact, the matrix is estimated by processing all first singular vectors and also all first singular values in a selected frequency range. In the proposed method, the typical two stage approach of traditional modal flexibility methods is avoided, and the intervention of an operator is limited to setting the values of a few parameters in the initial phase of the process. Numerical simulations and experimental data from a testbed structure were used to show the effectiveness of the proposed approach, and the analyses were performed by considering structures with different damage scenarios and damping properties.

A Lightweight and Efficient Method of Structural Damage Detection Using Stochastic Configuration Network.

With the advancement of neural networks, more and more neural networks are being applied to structural health monitoring systems (SHMSs). When an SHMS requires the integration of numerous neural networks, high-performance and low-latency networks are favored. This paper focuses on damage detection based on vibration signals. In contrast to traditional neural network approaches, this study utilizes a stochastic configuration network (SCN). An SCN is an incrementally learning network that randomly configures appropriate neurons based on data and errors. It is an emerging neural network that does not require predefined network structures and is not based on gradient descent. While SCNs dynamically define the network structure, they essentially function as fully connected neural networks that fail to capture the temporal properties of monitoring data effectively. Moreover, they suffer from inference time and computational cost issues. To enable faster and more accurate operation within the monitoring system, this paper introduces a stochastic convolutional feature extraction approach that does not rely on backpropagation. Additionally, a random node deletion algorithm is proposed to automatically prune redundant neurons in SCNs, addressing the issue of network node redundancy. Experimental results demonstrate that the feature extraction method improves accuracy by 30% compared to the original SCN, and the random node deletion algorithm removes approximately 10% of neurons.

A New Optimal Sensor Location Method for Double-curvature Arch Dams: A Comparison with the Modal Assurance Criterion (MAC)

Determining the optimal location of sensors in order to identify modal parameters in large structures such as dams is one of the most important and widely used topics in damage detection and health monitoring of structures. In this research, the modal parameters including the natural frequency and mode shape of two arched concrete dams have been calculated using the finite element method for healthy and damaged dams. The reduction of the elastic modulus of concrete in different parts and percentages has been used as the degree of damage. Then, using the modal confidence criterion (MAC) method, the optimal location of the sensors is determined, then the results of this method are compared with the new method. The results show that in both dams, the new method matches the MAC method with 90% accuracy. This new method is a fast and suitable measure to determine the optimal location of sensors in arched concrete dams.

A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection

In recent years, convolutional neural networks (CNNs) have demonstrated promising results in detecting structural damage. However, their architectures often overlook spatial and temporal effects simultaneously. This limitation can result in the loss of valuable information and an incapability to fully capture the complexity of the data, ultimately leading to reduced accuracy and suboptimal performance. This study proposes an intuitive three-dimensional CNN architecture that takes into account vibration history along with sensor spatial relations based on their relative positions. Furthermore, a multi-task learning (MTL) approach is suggested, which is a powerful approach for performing multiple tasks with a single network. The proposed 3D CNN method has been employed to detect single and double damage cases in an experimental steel frame through conventional classification alongside the transfer learning (TL). Moreover, MTL is used to detect single and double damage scenarios with a single unified network, which evaluates damage presence in separate tasks. The 3D CNN fulfilled state-of-the-art performance and 100% accuracy in detecting structural damage in almost all experiments. Additionally, the MTL model achieved promising results even in the presence of severe imbalanced classes of data. Furthermore, it was observed that the utilization of TL resulted in a notable reduction of computation time by 68% and the number of trainable parameters by 90% with the same level of accuracy in double-damage cases.

Structural damage detection of elevated circular water tank and its supporting system using geodetic techniques

Structural damage detection of elevated circular water tank and its supporting system using geodetic techniques

Bridge anomaly detection based on reconstruction error and structural similarity of unsupervised convolutional auto-encoder

This study presents a novel bridge anomaly detection approach that employs the reconstruction error and structural similarity of an unsupervised convolutional auto-encoder. The presence of structural damage in a bridge typically results in changes in its vibration signals, and thus, the use of these signals for structural damage detection (SDD) has been widely investigated, with many methods relying on supervised learning. However, such existing SDD methods based on the supervised learning require prior knowledge of the damage states and cannot process monitoring data in real-time, thereby limiting their application to in-service bridges. To address this challenge, the authors propose the use of a convolutional auto-encoder as the reconstruction algorithm for real-time vibration signals. The auto-encoder is trained using normal signals and then used to reconstruct new inputs (either normal or abnormal). Two damage indicators (reconstruction error and structural similarity) are then calculated based on the reconstruction results and clustered to detect abnormal signals. The proposed approach was applied to the detection of various abnormalities in the old ADA Bridge, the results were 100% accurate, and about a 10% increase in accuracy was observed when compared to other control experiments. These results demonstrate the effectiveness of the proposed approach, with the auto-encoder achieving excellent reconstruction results for normal signals and clear discrepancies for abnormal signals. The proposed method was also validated on a cable-stayed bridge and an arch bridge, demonstrating its wide applicability in bridge anomaly detection.

Structural damage detection based on modal strain energy assurance criterion using adaptive region shrinkage assisted IGOA

The efficiency in high-dimension optimization, premature convergence and noise robustness are common challenges that restrict the application of swarm-based algorithms in structural damage detection (SDD). In order to overcome these limitations, an improved grasshopper optimization algorithm (IGOA) based SDD approach assisted with adaptive region shrinkage strategies is proposed using modal strain energy assurance criterion. Firstly, the effectiveness of GOA in handling SDD is investigated, and an improved version is subsequently introduced with two adaptive region shrinkage strategies: adaptive Lévy flight mechanism and elite opposition-based learning, aiming to achieve more accurate and stable SDD results. Additionally, a novel objective function is defined, combining the frequency change ratio (FCR) and modal strain energy assurance criterion (MSEAC). To assess the performance of the proposed algorithm, a comparative analysis is conducted with four other selected swarm intelligent algorithms and GOAs with individual modification using four CEC benchmark functions. The results demonstrate that the proposed algorithm achieves improved convergence rate and accuracy. Furthermore, the effectiveness and efficiency of the proposed algorithm in SDD are validated through two numerical simulations and an experimental test. The SDD results obtained from both numerical simulations and experimental tests confirm the accuracy and robustness of the proposed method.

The influence of model and measurement uncertainties on damage detection of experimental structures through recursive algorithms

The influence of model and measurement uncertainties on damage detection of experimental structures through recursive algorithms