Vibration-based Structural Damage 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.

Vibration-based structural damage detection via phase-based motion estimation using convolutional neural networks

Detection of structural damage is a major concern for engineers. In recent years, convolutional neural networks (CNNs) have been used for feature extraction and classification of vibration signals that reveal structural damage. Damage detection by CNNs greatly depends on high-quality learning data which are usually difficult to be obtained in actual engineering scenarios. To solve this problem, we combine phase-based motion estimation (PME) with the use of CNNs. By PME method, each pixel in a video can be regarded as a separate displacement sensor. Thus, it is possible to obtain millions of vibration signals from a single video, greatly facilitating CNN applications. We used a two-story steel structure for experimental validation. It was demonstrated that only one measured video sample obtained under each structural condition is possible to train a CNN model accurately detecting the location and severity of bolt looseness damage. This verified the outstanding performance of the proposed method.

Structural damage detection by integrating robust PCA and classical PCA for handling environmental variations and imperfect measurement data

This paper proposes a vibration-based structural damage detection approach considering the effects of uncertainties, including environmental variations and random errors that possibly stem from measurement and automatic modal identification. The existing methods that only employ the classical Principle Component Analysis (PCA) have been demonstrated effective to remove the effects of environmental variations while extremely sensitive to random errors. Therefore, the robust PCA is firstly introduced to remove the random errors, especially outliers, that significantly corrupt the low-rank property of the stacked damage sensitive feature (DSF) matrix. Then, the classical PCA is used to extract the environmental variation-free residues, which are inherently damage-dependent and can be used to detect the existence of damage. The problem of missing data is also considered in this study. It is tackled by adding virtual random errors to the locations of missing entities and thus can be addressed by the introduced robust PCA. The advantages of the proposed approach include: (1) Handling the random error-contaminated DSF data regardless of the error’s amplitude, which is an intractable problem for the existing classical PCA-based methods to consider the environmental effects; (2) Damage detection process can be automatic since the missing data can be automatically predicted and the random errors are not required to be manually distinguished. The effectiveness and performance of the proposed method are demonstrated on a numerical beam structure and an experimentally tested wooden bridge model.

Railway track support condition assessment—Initial developments on a vehicle-based monitoring solution through modal analysis

Nowadays, there are multiple initiatives showing a renewed interest on railway transport of goods and passengers around the world. Thus, an efficient management of railway infrastructures, both at the operational level and in terms of economic profitability, is not only desirable but also corresponds to an area of ongoing research. In order to contribute to these efforts, an alternative and novel methodology to evaluate railway track support conditions is presented here, based on modal analysis of the characteristic frequencies of the multi-element system composed by a railway infrastructure and an instrumented vehicle moving over it. This methodology belongs to the group of vibration-based structural damage identification methods, and is focused on observing the characteristic frequencies of this multi-element system, which can be correlated with changes in the physical properties of the railway infrastructure under analysis. An important feature of the proposed methodology is that it should enable the collection of information regarding the conditions of the substructure of a railway infrastructure. By performing this assessment of a railway infrastructure over its length, and over time by comparing different rides over the same railway stretch, important information can be gathered regarding the support conditions of the track. This paper presents a complete description on the current stage of development of the proposed methodology, along with the theoretical model that serves as the basis to interpret the collected data. Preliminary verification of this methodology is performed through the analysis of two case studies regarding the passage of an instrumented vehicle over two underpasses. The results obtained so far show that the proposed methodology can provide relevant information regarding the support conditions of railway tracks.

A hybrid methodology for structural damage detection uniting FEM and 1D-CNNs: Demonstration on typical high-pile wharf

Vibration-based structural damage detection (SDD) has been a subject of intense research in structural health monitoring (SHM) for large civil engineering structures over the decades. The performance of the conventional SDD approaches predominantly relies on the rational choices of the damage feature and classifier. Hand-crafted features or fixed classifiers would not be the optimal choice for all structural damaged scenarios. This paper proposes a novel, quick and precise real-time SDD framework for high-pile wharf foundations using a combination of finite element modeling and 1D convolutional neural networks (CNNs). The distinct advantage of this method lies in extracting the damage-related features from the raw displacement response directly and automatically, and the computational complexity of the compact 1D CNNs is significantly lower because the data processing involves only simple 1D operations. The results show that the presented 1D CNNs have a superior ability to accurately identify the occurrence and location of damage in real time. In addition, the comprehensive performance of the CNNs trained by the displacement response dataset in component form is significantly better than that based on the dataset in absolute value form. The results also demonstrated that although the proposed CNNs are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure, the vertical component still has a positive effect on the improvement of the generalization and robustness of the CNNs.

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

An Investigation of Particle Swarm Optimization Topologies in Structural Damage Detection

In the past few decades, vibration-based structural damage detection (SDD) has attracted widespread attention. Using the response data of engineering structures, the researchers have developed many methods for damage localization and quantification. Adopting meta-heuristic algorithms, in which particle swarm optimization (PSO) is the most widely used, is a popular approach. Various PSO variants have also been proposed for improving its performance in SDD, and they are generally based on the Global topology. However, in addition to the Global topology, other topologies are also developed in the related literature to enhance the performance of the PSO algorithm. The effects of PSO topologies depend significantly on the studied problems. Therefore, in this article, we conduct a performance investigation of eight PSO topologies in SDD. The success rate and mean iterations that are obtained from the numerical simulations are considered as the evaluation indexes. Furthermore, the average rank and Bonferroni-Dunn’s test are further utilized to perform the statistic analysis. From these analysis results, the Four Clusters are shown to be the more favorable PSO topologies in SDD.

Baseline-Free Structural Damage Identification for Beam-Like Structures Using Curvature Waveforms of Propagating Flexural Waves.

Curvatures in mode shapes and operating deflection shapes have been extensively studied for vibration-based structural damage identification in recent decades. Curvatures of mode shapes and operating deflection shapes have proved capable of localizing and manifesting local effects of damage on mode shapes and operating deflection shapes in forms of local anomalies. The damage can be inversely identified in the neighborhoods of the anomalies that exist in the curvatures. Meanwhile, propagating flexural waves have also been extensively studied for structural damage identification and proved to be effective, thanks to their high damage-sensitivity and long range of propagation. In this work, a baseline-free structural damage identification method is developed for beam-like structures using curvature waveforms of propagating flexural waves. A multi-resolution local-regression temporal-spatial curvature damage index (TSCDI) is defined in a pointwise manner. A two-dimensional auxiliary TSCDI and a one-dimensional auxiliary damage index are developed to further assist the identification. Two major advantages of the proposed method are: (1) curvature waveforms of propagating flexural waves have relatively high signal-to-noise ratios due to the use of a multi-resolution central finite difference scheme, so that the local effects of the damage can be manifested, and (2) the proposed method does not require quantitative knowledge of a pristine structure associated with a structure to be examined, such as its material properties, waveforms of propagating flexural waves and boundary conditions. Numerical and experimental investigations of the proposed method are conducted on damaged beam-like structures, and the effectiveness of the proposed method is verified by the results of the investigations.

Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.

A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit

With the extensive use of structural health monitoring technologies, vibration-based structural damage detection becomes a crucial task in both academic and industrial communities. Following the noteworthy trends of data-driven paradigms in recent years, some solutions have been released to identify, localize, and classify damages via deep neural networks. However, some deficiencies still exist for effective damage-intensive feature extraction and representation. To overcome such a problem, this paper proposes a novel end-to-end structural damage detection neural model by taking the advantages of the Convolutional Neural Network and Bidirectional Gated Recurrent Unit in parallel. The well-known IASC-ASCE benchmark and TCRF dataset are used for evaluation. The experimental results show that the proposed approach can achieve a better detecting effect than other existing manners.

Structural damage detection in steel frames using modal strain energy method and genetic algorithm

In recent years, the field of structural health monitoring (SHM) has been receiving the attention of many researchers. In particular, vibration-based structural damage detection methods have proven to be highly effective in civil engineering, mechanical engineering, aerospace engineering, … This paper presents a two-step procedure for damage detection in steel plane frame structures. In the first step, the damage locations in the frame are detected by Modal Strain Energy Index (MSEBI). This index is calculated on the difference from the modal strain energy value of every element before and after damage. In the second step, the damage extents are identified by Genetic Algorithm (GA). The objective function is established on the basis of the change of modal strain energy. A plane steel frame model is built by the finite element method. The results of the frame’s free vibration analysis are the input data of the proposed damage detection procedure. In order to improve the accuracy of the damage localization, the MSEBI is determined using the combination of the frame's first four bending mode shapes. The proposed method gives highly accurate results in detecting the damage’s location and extent for various damage scenarios.

Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes

Vibration-based structural damage identification through optimization techniques has become an interesting research topic in recent years. Dynamic characteristics such as frequencies and mode shapes are used to construct the objective function. The objective functions based on only frequencies are not very sensitive to damage in large structures. However, objective functions based on both mode shapes and frequencies are very effective. In real measurement condition, the number of installed sensors is limited, and there are no economic reasons for measuring the mode shapes at all degrees of freedom. In this kind of circumstances, mode expansion methods are used to address the incompleteness of mode shapes. In this article, the system equivalent reduction and expansion process is applied to determine the unmeasured mode shapes. Two experimental examples including a cantilever beam and a truss tower are investigated to show system equivalent reduction and expansion process’ efficiency in estimating unmeasured mode shapes. The results show that the technique used for expansion is influential. Damage identification is formulated as an optimization problem, and the residual force vector based on expanded mode shapes is considered as an objective function. In order to minimize the objective function, grey wolf optimization and Harris hawks optimization are used. Numerical studies on a 56-bar dome space truss and experimental validation on a steel frame are performed to demonstrate the efficiency of the developed approach. Both numerical and experimental results indicate that the combination of the grey wolf optimization and expanded mode shapes with system equivalent reduction and expansion process can provide a reliable approach for determining the severities and locations of damage of skeletal structures when it compares with those obtained by Harris hawks optimization.

A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure.

Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on one-dimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF-1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios. The damage identification accuracy of the framework was examined and compared with others by using different dataset types and intelligent algorithms. Specifically, compared with time series (TS) and fast Fourier transform (FFT)-based frequency-domain signals, the TF signals exhibited more significant damage-sensitive features and stronger stability under excitation interference. The utilization of 1D CNN, on the other hand, exhibited some unique advantages over other machine learning algorithms (e.g., traditional artificial neural networks (ANNs)), particularly in aspects of computation efficiency, generalization ability, and noise immunity when treating massive, high-dimensional datasets. The developed TF-1D CNN damage identification framework was demonstrated to have practical value in future applications.

Elliptical Method for Damage Identification in Carbon Fibre Reinforced Polymer Laminates

Oftentimes, researchers in the area of vibration-based structural health monitoring (SHM) and damage detection focused their attention on the global properties of structures, which are modal frequencies and modal damping factors. However, the effect on the local properties for SHM, that is, modal constants, has not been extremely explored. In this paper, the elliptical plane modal identification method is proposed to be used as a damage identification method itself. It is observed that when the receptance is plotted in the elliptical plane, the area of the ellipse formed close to the resonant frequencies (which depends on the modal constants) can be used to detect damage, namely, composite carbon fibre reinforced polymer (CFRP) rectangular plates. Although a mathematical correlation has not been established yet, results show that the method is sensitive to the presence of damage in the test plates, as the area of the ellipse changes with damage.

Gapped Gaussian smoothing technique for debonding assessment with automatic thresholding

Sandwich structures are subjected to imperfect bonding or debonding caused by defects during the manufacturing process, by fatigue, or by impact loads. In this context, their safety and functionality can be improved with the implementation of vibration-based structural damage assessment methodologies. These methodologies involve the computation of second or higher order displacement derivatives, which are often obtained using the central difference method. Nevertheless, this method propagates and amplifies the measurement errors and noise. Therefore, a Gaussian process (GP) regression model to build smoothed (noise-free) curvature mode shapes from noisy experimental mode shape displacements is presented in this paper. The proposed baseline-free debonding assessment approach combines the gapped smoothing (GS) method, curvature mode shapes estimated using a GP regression, and the valley-emphasis method to automatically find damaged regions. Experimental results indicate that our approach performs better than the conventional GS method in the presence of experimental noise.

Output-only structural damage detection based on transmissibility measurements and kernel principal component analysis

Frequency response functions have been employed as damage-sensitive features in the vibration-based structural damage detection. However, the need for measuring the excitation forces arises as a remarkable limitation on the application of those features in real-world applications. As an alternative, transmissibility measurements can be explored as features with output-only nature, which implies the need for measuring only the response signals. In this paper, an output-only damage detection method is proposed, combining transmissibilities with kernel principal component analysis (KPCA). This technique is based on the pattern recognition paradigm for structural health monitoring, where feature extraction and feature classification phases are considered. In the first phase, the dimensionality of the transmissibilities is appropriately reduced by applying the KPCA algorithm. In the second phase, an outlier detection strategy is used to determine the condition of the instrumented structure. The possibility of clustering in the high-dimensional space mapped by KPCA is also reported and discussed. The proposed method is experimentally validated with transmissibilities acquired, under distinct structural conditions, from a laboratory steel beam instrumented with several accelerometers. The results demonstrate that the output-only method has high potential to be applied in a wide range of monitoring solutions, where economic issues and life-safety are primary motivations.

Structural Health Monitoring for Multi-story Shear Frames Based on Signal Processing Approach

Non-destructive vibration-based global structural health monitoring and damage detection methods are divided into modal-based and signal-based methods. There is much research in these areas, but each one has some advantages and disadvantages. The combination of these two areas can greatly overcome these disadvantages and thus can enhance the robustness of the damage detection methods. This article presents a signal-modal-based structural health monitoring technique for multi-story shear buildings subjected to an earthquake event. The proposed method utilizes the combination of wavelet transform and FFT as signal processing tools and also Hilbert transform with the aim of final modal parameters extraction, in order to determine the existence, location, and extent of damage in the multi-story shear frames. In fact, extraction of final modal parameters from the acceleration response signals is desired. To demonstrate the capabilities of the proposed algorithm, numerical simulations are performed on a four-story two-bay and ten-story two-bay shear frame with different damage scenarios using OpenSees. The results show that this method can detect the occurrence, location, and severity of the damage with good accuracy even in different levels of multi-damage scenarios and also in the presence of measurement noise.

Vibration-Based Structural Damage Identification under Varying Temperature Effects

AbstractVibration-based methods are promising for damage identification; however, their capabilities for damage identification under temperature variations are usually limited. In the paper, a vibr...

Damage detection of rotating wind turbine blades using local flexibility method and long-gauge fiber Bragg grating sensors

Wind turbines are a cost-effective alternative energy source; however, their blades are susceptible to damage. Therefore, damage detection of wind turbine blades is of great importance for condition monitoring of wind turbines. Many vibration-based structural damage detection techniques have been proposed in the last two decades. The local flexibility method, which can determine local stiffness variations of beam-like structures by using measured modal parameters, is one of the most promising vibration-based approaches. The local flexibility method does not require a finite element model of the structure. A few structural modal parameters identified from the ambient vibration signals both before and after damage are required for this method. In this study, we propose a damage detection approach for rotating wind turbine blades using the local flexibility method based on the dynamic macro-strain signals measured by long-gauge fiber Bragg grating (FBG)-based sensors. A small wind turbine structure was constructed and excited using a shaking table to generate vibration signals. The structure was designed to have natural frequencies as close as possible to those of a typical 1.5 MW wind turbine in real scale. The optical fiber signal of the rotating blades was transmitted to the data acquisition system through a rotary joint fixed inside the hollow shaft of the wind turbine. Reversible damage was simulated by aluminum plates attached to some sections of the wind turbine blades. The damaged locations of the rotating blades were successfully detected using the proposed approach, with the extent of damage somewhat over-estimated. Nevertheless, although the specimen of wind turbine blades cannot represent a real one, the results still manifest that FBG-based macro-strain measurement has potential to be employed to obtain the modal parameters of the rotating wind turbines and then locations of wind turbine segments with a change of rigidity can be estimated effectively by utilizing these identified parameters.

1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data

Structural damage detection has been an interdisciplinary area of interest for various engineering fields. While the available damage detection methods have been in the process of adapting machine learning concepts, most machine learning based methods extract “hand-crafted” features which are fixed and manually selected in advance. Their performance varies significantly among various patterns of data depending on the particular structure under analysis. Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but also yields a superior computational efficiency. 1D CNNs have recently achieved state-of-the-art performance in vibration-based structural damage detection; however, it has been reported that the training of the CNNs requires significant amount of measurements especially in large structures. In order to overcome this limitation, this paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure. This approach is verified using the experimental data of the Phase II benchmark problem of structural health monitoring which had been introduced by IASC-ASCE Structural Health Monitoring Task Group. As a result, it is shown that the enhanced CNN-based approach successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.