Vibration-based Damage Detection Methods Research Articles

Vibration-based damage detection method with tunable resolution for composite laminates

Composite laminates are increasingly applied in advanced engineering structures, but they are prone to be damaged in service. However, multiple defects with different sizes challenge the current damage detection methods to identify all of them without a baseline. In this paper, a baseline-free damage identification method based on vibration is developed, and a novel damage index with tunable resolution of detection is proposed based on two-dimensional continuous wavelet transform by manipulating wavelet parameters. The relationship between the smallest detectable damage size and wavelet parameters is explored quantitatively, and the effectivity and reliability of proposed method are numerically and experimentally verified to directly detect damage in composite laminates without baselines. By a comparison with other vibration-based damage detection methods, results show that the proposed method can precisely locate and visualize multi-damage with determinate resolution of detection, and can realize multi-resolution of damage detection for identifying various sizes of defects.

Application of variational mode decomposition in vibration-based bridge damage detection

Bridges are critical infrastructure that require continuous structural health monitoring to ensure safety. Vibration-based methods use changes in dynamic response for damage detection. In this study, variational mode decomposition (VMD), a signal analysis technique, was applied to detect damage in a three-span beam bridge model subjected to moving loads. A finite-element model was developed to represent the bridge–vehicle interaction and damage was simulated by reducing the flexural stiffness of the bridge. Sensors placed on each span recorded the vibrations during vehicle passage. VMD was used to decompose the signals for analysis of damage indications. Different damage levels (30–50% stiffness reduction) and locations (each span) were evaluated. The vehicle speed was also varied to assess its effect. Damage was identified as peaks in the instantaneous energy diagrams of decomposed signals, correctly indicating the axle locations. Detection was improved with sensors nearer the damage and lower vehicle speed. The results demonstrate the ability of VMD to detect and locate damage without a baseline, indicating its potential as a non-baseline vibration-based damage detection method for bridges.

Detection of Structural Damage in a Shaking Table Test Based on an Auto-Regressive Model with Additive Noise

Damage identification plays an important role in enhancing resilience by facilitating precise detection and assessment of structural impairments, thereby strengthening the resilience of critical infrastructure. A current challenge of vibration-based damage detection methods is the difficulty of enhancing the precision of the detection results. This problem can be approached through improving the noise reduction performance of algorithms. A novel method based partially on the errors-in-variables (EIV) model and its total least-squares (LS) algorithm is proposed in this study. Compared with a classical damage detection approach involving adoption of auto-regressive (AR) models and the least-squares (LS) method, the proposed method accounts for all the observation errors as well as the relationships between them, especially in an elevated level of noise, which leads to a better accuracy. Accordingly, a shaking table test and its corresponding finite element simulation of a full-scale web steel structure were conducted. The acceleration time-series output data of the model after suffering from different seismic intensities were used to identify damage using the presented detection method. The response and identification results of the experiment and the finite element analysis are consistent. The finding of this paper indicated that the presented approach is capable of detecting damage with a higher accuracy, especially when the signal noise is high.

A statistical and reliability approach to vibration-based health monitoring in composite structures

In this paper, a novel statistical vibration-based damage detection method is developed considering uncertainties in measured resonance frequencies. The proposed method is based on the application of resonance frequencies as the most accurate and easiest measurable vibration feature. For proof of efficiency of the proposed method, case studies were undertaken using two identical composite plates, one delaminated and the other pristine. In this respect, the frequency response functions (FRFs) were measured and used as the main input to the Resonance Detection Algorithm as the proposed method. Applying these FRFs to a Resonance Detector Function can determine the resonant frequencies and their statistical distribution. Through the statistical distributions of the corresponding resonant frequencies, their reliability of detecting damage has been obtained via the beta distribution. By observing the damage detection reliability of the two sets of corresponding resonant frequencies, it has been determined that the changes in natural frequencies are due to structural changes and not random errors through measurement.

Damage identification in steel frames using dual-criteria vibration-based damage detection method and artificial neural network

Vibration-based damage identification methods have proven to be effective in identifying damage in various structures by employing modal parameters. This paper presents and develops a two-stage multi-criteria damage detection method based on modified damage indices (DIs), specifically modal flexibility and modal strain energy-based damage indices, and artificial neural network (ANN) to locate and quantify damage in steel frames. Only information from the first three bending modes was used to calculate the DIs. The location of damages is detected using DIs in the first stage, and two separate artificial neural networks are trained based on the DIs as input parameters in the second stage. The feasibility and performance of the proposed method were assessed by applying different single and multiple damage scenarios to a 3D finite element model of a simply supported steel beam and a steel frame of an industrial shed. The results demonstrate the efficiency and competency of the proposed non-destructive method for damage detection in such steel frames.

Model-Based Damage Localization Using the Particle Swarm Optimization Algorithm and Dynamic Time Wrapping for Pattern Recreation.

Vibration-based damage detection methods are a subcategory of Structural Health Monitoring (SHM) methods that rely on the fact that structural damage will affect the dynamic characteristic of a structure. The presented methodology uses Finite Element Models coupled with a metaheuristic optimization algorithm in order to locate the damage in a structure. The search domains of the optimization algorithm are the variables that control a parametric area, which is inserted into the FE model. During the optimization procedure, this area changes location, stiffness, and mass to simulate the effect of the physical damage. The final output is a damaged FE model which can approximate the dynamic response of the damaged structure and indicate the damaged area. For the current implementation of this Damage Detection Framework, the Particle Swarm Optimization algorithm is used. As an effective metric of the comparison between the FE model and the experimental structure, Transmittance Functions (TF) are used that require output only acceleration signals. As with most model-based methods, a common concern is the modeling error and how this can be surpassed. For this reason, the Dynamic Time Wrapping (DTW) algorithm is applied. When damage occurs in a structure it creates some differences between the Transmittance Functions (TF) of the healthy and the damaged state. With the use of DTW, the damaged pattern is recreated around the TF of the FE model, while creating the same differences and, thus, minimizing the modeling error. The effectiveness of the proposed methodology is tested on a small truss structure that consists of Carbon-Fiber Reinforced Polymer (CFRP) filament wound beams and aluminum connectors, where four cases are examined with the damage to be located on the composite material.

Study on the dynamic behaviour of Bafgh-Bandar Abbas lane railway bridge for structural health monitoring purpose

ABSTRACT This paper uses the vibration-based damage detection method for structural health monitoring of a Railway Bridge. Hence, the bridge had been exactly modelled in finite element analysis software. Due to the elastomeric bridge bearing, direct calculating of the natural frequencies and extracting the mode shapes of the bridge is not suitable and effective. Therefore, a time-dependent transient analysis of the train movement on the bridge is done and then the vibrations of all truss cells during the train crossing period were extracted. Afterwards, the relevant data is transformed, which represents the natural frequencies of the bridge vibration and their amplitude at each point. In the following, the mode shapes of vibration and the distribution of vibration energy are calculated. Applying any artificial damages in the model, occurring changes in the natural frequencies, mode shapes, and the vibration energies of the modes, are examined. Consequently, detecting and locating damages in the structure has been done with acceptable accuracy. Finally, for validation of the results, an accelerometer is installed on the bridge truss in the middle part of the bridge to extract the acceleration of bridge vibration at the train crossing time interval. In the end, the results are compared and presented.

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.

Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm

The continuous development of new materials and larger and/or more complex structures drives the need for the development of more robust, accurate, and sensitive Structural Health Monitoring (SHM) techniques. In the present work, a novel vibration-based damage-detection method that contributes into the SHM field is presented using Metaheuristic algorithms coupled with optimal Finite Element Models that can effectively localize damage. The proposed damage-detection framework can be applied in any kind of detailed structural FE model, while requiring only the output information of the dynamic response of the structure. It can effectively localize damage in a structure by highlighting not only the affected part of the structure but also the specific damaged area inside the part. First, the optimal FE model of the healthy structure is developed using appropriate FE model updating techniques and experimental vibration measurements, simulating the undamaged condition. Next, the main goal of the proposed method is to create a damaged FE model that approximates the dynamic response of the damaged structure. To achieve this, a parametric area is inserted into the FE model, changing stiffness and mass to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed damage-detection framework. On this specific implementation of the framework, the Particle Swarm Optimization (PSO) algorithm is selected which has been used for a wide variety of optimization problems in the past. On the PSO’s search space, two parameters control the stiffness and mass of the damaged area while additional location parameters control the exact position of the damaged area through the FE model. For effective damage localization, the Transmittance Functions from acceleration measurements are used which have been shown to be sensitive to structural damage while requiring output-only information. Finally, with proper selection of the objective function, the error that arises from modeling a physical damage with a linear damaged FE model can be minimized, thus creating a more accurate prediction for the damaged location. The effectiveness of the proposed SHM method is demonstrated via two illustrative examples: a simulated small-scale model of a laboratory-tested vehicle-like structure and a real experimental CFRP composite beam structure. In order to check the robustness of the proposed method, two small damage scenarios are examined for each validation model and combined with random excitations.

Low-story damage detection of buildings using deep neural network from frequency phase angle differences within a low-frequency band

Lower stories of buildings are much more vulnerable due to the critically dead load from high stories. Vibration-based damage detection methods are commonly used to quantify damage with indicators and early assess structural deterioration. However, these indicators are usually not directly related to structural performance and can be distorted by the modeling errors or sensing uncertainties. In addition, damage detection of structures can be realized from sensor data by the remaining useful life estimation framework in machine learning. Meanwhile, a machine learning model, which detects structural damage, can be trained by the generated data from a numerical/identified model, e.g., phase angle differences of structural transfer functions. Moreover, the bounded nature of phase angles coincides with the needs of machine learning algorithms, and the combined stiffness and damping changes involved in damaged structures can also be considered in a learning model. Therefore, this study proposes a neural network damage detection method based on frequency response phase angle differences for low-story damage of buildings. In this proposed method, a simplified model is established from identified modal parameters of a structure and optimized using the least-squared stiffness approach, and the transfer function phase angle differences are generated from the model considering the stiffness and damping changes in various damage scenarios. The generated data are exploited to train a deep learning model which predicts the residual stiffness percentages as the condition parameters in the remaining useful life estimation framework. In this study, a numerical example provides detailed procedure to develop the neural network damage detection model and evaluate the effectiveness of the learning model. The proposed method is also employed in a scaled six-story steel-frame building for experimental verification. As seen in the results, the neural network damage detection method can estimate the residual stiffness percentages in the lower stories of a building based on frequency response phase angle differences.

Structural damage detection via phase space based manifold learning under changing environmental and operational conditions

The feasibility and performance of existing vibration-based damage detection methods to real world civil engineering structures are inevitably affected by the varying environmental and operational conditions. Reliable damage detection methods with damage features that are sensitive to structural condition change but robust to environmental and loading effects are desirable for practical applications. This paper proposes a novel structural damage detection approach based on manifold learning for the effective condition assessment of real-world structures under environmental and operational conditions. The phase space representation of the vibration characteristics is reconstructed using the identified natural frequencies of structures. Then, the intrinsic nonlinear manifold between the environmental variables and natural frequencies in the high dimensional phase space is projected to a low-dimensional representation via manifold learning. The Gaussian process regression technique is introduced to extract reliable damage index from the learned manifold structure. The effectiveness and superiority of the proposed approach are demonstrated by two real-world engineering structures, that is, the Dowling Hall Footbridge and Z24 bridge. Damage detection results obtained from the proposed approach are compared with those from the current state-of-the-art Kernel PCA method, which is a representative nonlinear dimensionality reduction method to alleviate the environmental effects. The results demonstrate that the proposed approach is sensitive to structural damage but insensitive to changes in environmental and operational conditions. More importantly, the nonlinear environmental effects can be efficiently characterized by the proposed approach, using only partial datasets with environmental variations in the training datasets.

A channel-spatial-temporal attention-based network for vibration-based damage detection

Structural health monitoring (SHM) is extremely vital for the diagnosis and prognosis of civil structures. As an important part of the SHM system, vibration-based damage detection (VBDD) methods have become a research hotspot with the development of sensor technologies. These methods are utilized to assess structural conditions or localize and classify damages. Recently end-to-end deep learning architectures have been widely used in VBDD tasks and achieved state-of-the-art results. However, there are seldom investigations on the attention mechanism in VBDD, which has been demonstrated as an effective module to extract features in other domains. In this paper, we propose a channel-spatial-temporal attention-based network to refine and enrich the discriminative sample-specific features in three dimensions, namely, channel, space, and time simultaneously. Specifically, the local and global block we designed is to extract the local and global spatial features adaptively, and the grouped self-attention is presented to extract the long- and short-term temporal features. Moreover, the squeeze-and-excitation block is selected to emphasize vital channels. Extensive experiments are conducted on three-span continuous rigid frame bridge scale model and IASC-ASCE benchmark datasets, and the results prove that the proposed method is superior to the existing state-of-the-art methods.

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.

Crack Detection in Plate-Like Structures Using Modal Strain Energy Method considering Various Boundary Conditions

Among vibration-based damage detection methods, one of the effective approaches for damage localization is the modal strain energy method. In this paper, the modal strain energy method is developed for damage detection in plate-like structures with various boundary conditions. Firstly, the theory of the modal strain energy method is briefly outlined. In order to overcome the limitation of measuring points, a central difference method is newly employed to compute the partial differential terms in the modal strain energy formula. Finite element analysis is conducted on an aluminum thin plate to obtain the mode shapes before and after the occurrence of damage. Feasibility of the proposed method is verified by investigating plates with different types of boundary conditions. A damage index is presented to identify the location and extent of crack in the plate-like structures. The analytical results show that the proposed method accurately identifies the crack in the plate structure with various types of boundary conditions by using appropriate mode shapes and damage threshold.

Bearing Damage Detection of a Bridge under the Uncertain Conditions Based on the Bayesian Framework and Matrix Perturbation Method

The bearing of a bridge, as a critical component, is important in the force transformation of the superstructure; however, due to the service condition and repeated impact load, the bearing is prone to be damaged but difficult to detect the damage; the present research has few studies that focused on the damage detection of the structural bearing. Meanwhile, practical engineering is always surrounded by variational environmental conditions, and sometimes, the element and bearing damage both exist in the structure. Thus, these uncertain conditions all cause inaccurate damage identification results using the vibration-based damage detection method. In order to detect the damage of the structural bearing and improve the precision, firstly, the structural dynamic characteristic equation considering uncertain conditions has been deduced; then, a damage detection framework constructed by the Bayesian theory and perturbation method has been developed in this article; a numerical example of an 8-span concrete continuous beam and a practical example of I-40 steel-concrete composite bridge are utilized to validate the feasibility of the proposed method, and single type and two types of damage cases are studied. The outcomes demonstrate that the damage of structural elements and bearings can be detected with high accuracy. The proposed method is of great applicability and good potential.

Subspace‐based Mahalanobis damage detection robust to changes in excitation covariance

In the context of detecting changes in structural systems, several vibration-based damage detection methods have been proposed and successfully applied to both mechanical and civil structures over the past years. These methods involve computing data-based features, which are then evaluated in statistical tests to detect damages. While being sensitive to damages, the data-based features are affected by changes in the ambient excitation properties that potentially lead to false alarms in the statistical tests, a characteristic that renders their use impractical for structural monitoring. In this paper, a damage detection method is presented that is robust to changes in the covariance of the ambient excitation. The proposed approach is based on the Mahalanobis distance of output covariance Hankel matrices, which are normalized with respect to possibly changing excitation properties. The statistical properties of the developed damage feature are reported, and used for efficient hypothesis testing. Its robustness towards changes in the excitation covariance is illustrated on numerical simulations and successfully tested on a numerical offshore foundation model.

Consideration of uncertainty in damage detection using interval analysis wavelet without baseline data

ABSTRACT The effectiveness of vibration-based damage detection (VBDD) method has been demonstrated by researchers to provide reliable results. However, the existence of uncertainties in measurement and modelling data hinders the accuracy of results obtained from VBDD. Researches have yielded favourable results by integrating probabilistic method. Despite these successes, the probabilistic method faces the problem of obtaining an unbiased probabilistic distribution of uncertainties. Furthermore, the probabilistic method involves long and complex computations. In dealing with these problems, the nonprobabilistic method that requires no assumptions of the uncertainties distribution was proposed. It involves estimating only the upper and lower bounds of the uncertain parameter. However, the success of the nonprobabilistic method is shortened by its reliance on baseline (undamaged) data that is often not available for existing structures. In this study, a nonprobabilistic interval analysis wavelet (NIAW) method to consider uncertainties in damage identification without using baseline healthy data is proposed. The proposed method is demonstrated by using a plate structure and applying the symmetrical properties of the plate structure. The wavelet coefficient of the plate mode shape is divided along the line of symmetry to obtain wavelet coefficients WL and WR , and the bounds (upper and lower) of WL and WR are estimated. The PoDE and wavelet coefficient increment factor (WCIF) are estimated to obtain damage identity by using the bounds of WL and WR . The product of PoDE and WCIF provides the value of DMI which indicates the level of damage severity. This method is demonstrated using numerical models of a steel plate. The results show that the proposed method accurately identifies damage when noise-contaminated mode shape data is applied.

An Ensemble Neural Network for Damage Identification in Steel Girder Bridge Structure Using Vibration Data

Damage detection has the ability to prevent the occurrence of unpredictable failures and increase the serviceability of structures. Vibration-based damage detection methods are due to the fact that the damages will change the dynamic characteristics of a structure, such as natural frequencies, mode shapes and damping ratios. Resultantly, structural capacity is usually impacted, which subsequently, adversely affects performance. Fortunately, artificial neural networks (ANNs) have emerged as one of the most powerful learning tools, inspired by biological nervous systems. Unsurprisingly, the said technique has been applied for structural damage identification in the past decades. Relatedly, the objective of this study was to investigate the potential of ensemble neural network-based damage detection techniques in a scaled steel girder bridge structure using dynamic parameters. Experimental and finite element analyses of the structure were performed to generate modal parameters and study the efficiency of the ensemble neural networks in order to improve structural damage identification. Pursuant to the damage identification results, the ensemble ANN-based damage identification method was able to detect and locate damage with a high level of accuracy.

Assessment of a Real-life Concrete Bridge Structure using Vibration-based Damage Detection Method

Structural damage is usually presented in the form of decrease in stiffness resulting changes in the dynamic behavior of the structure, and natural frequency of the structure being one of the dynamic variables. A vibration-based damage detection method was employed to evaluate the structural health condition of a deteriorated bridge. The bridge has been closed for the traffic due to owing the extreme deteriorated health condition because of the immense existence of chloride ion in the airborne particles of the saline environment. This study presents, the comparison of natural frequencies of the year 2017 to that acquired in the year 2016 using vibration-based damage detection method. Even though the bridge was not exposed to the service load during the study period, yet, the results manifested decrement in natural frequencies revealing decline in structural stiffness in this period.

A method to detect cracks in the beams with imperfect boundary conditions

Non-destructive testing of structures involving vibration-based damage detection methods implies knowing the beam’s boundary conditions. For perfect boundary conditions, numerous damage detection methods are developed and ensure more or less accurate estimation of the crack type, position, and severity. On the contrary, for imperfect boundary conditions, which are the real ones, there are only a few dedicated works. This paper presents a methodology that allows the identification of the weak clamping and the crack if both exist. The weak clamping is modelled as a defect that produces an identical relative frequency shift (RFS) for all vibration modes. Therefore, to find the two defects namely the real crack and defect simulating the weakly clamped end, we apply the principle of superposition. The method is implemented as an application written in the Python programming language. Tests show that defects are successfully identified even if there are uncertainties about the fixing of the beam.